“Not all who wander are lost”
Can a UAV learn a city by heart — no GPS, no map
on board, just a $4 flight computer?
The bottom line
Yes! We found a special-purpose
neural memory architecture that ‘remembers’ an exact
geo-location, given an in-flight daytime picture of Berlin.
No GPS, no internet, no map on board, just a
3.1 MB file of weights. It works on 96.5% of camera
frames, and it took 81 experiments and $364 to
find.
96% of camera frames give a usable position fix
0% — never 50% 100% — every frame confident and within 100 m. The
loop minimises the mission penalty :
0.040 = 4% no fix
+ 0.5% wrong fix — a confident error
counts double.
5 research eras
81 experiments
$364.64 total cost
What we tried
We tried: ImageNet-pretrained backbones, separate day
and night specialists behind a dispatcher, contrastive pretraining on the
imagery itself, retrieval against a stored fingerprint of the city, dense
per-patch coordinate voting, learned relighting, calibrated abstention, and
more.
Our first 76 experiments failed. Hard. The flaw was
the harness itself; fixing it unlocked the ‘memory-in-weights’
architecture.
1
The question One frame, no map, no internet — and it has to know when it doesn’t know.
A UAV flies over a city it has trained on. Its camera takes one picture
straight down. From that picture alone: where is it?
And — how much should the aircraft trust the answer? A wrong fix
flown with confidence is worse than no fix at all. The model has to know when
it doesn't know.
what the UAV sees — one real 128 m frame. No map aboard, no internet, no GPS.→
lat 52.49995
lon 13.35294
confidence high
what it must return — the true answer for that frame, computed on board.The first baseline answered this frame 2.01 km away at confidence 0.99 — confident and wrong, the one answer a UAV must never get. The current model answers that same frame within 24.1 m .
This, on a map, is the goal. Every dot is one held-out viewpoint: ground the model trained on, framed from a position and heading it has never seen. Left is where this started. Right is where it is now. Turning red into green — without ever being confidently wrong — is the whole project.
where this started — measured 2.001 · usable fixes 0% →
where we are — measured 0.040 · usable fixes 96.5% 2
The answer Stop asking for a coordinate. Ask which tile.
INFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 camera frame one daytime frame frozen contract convolutional encoder Conv 3×3, stride 2 ×4 · BN + ReLU 64²×32 8²×160 160-d GAP FC → 2,970 cell logits softmax probability field 55×54 map cells · 128 m each 3×3 pooled centroid mass-weighted balance point of the winning cell + 8 neighbours within-cell offset tanh · ½ cell frozen contract (lat, lon, confidence) position fix + confidence confidence 0–1 = pooled mass over the whole 3×3 block may abstain instead of guessing soft cross-entropy vs a bilinear tent over the 4 nearest cell centres — border views taught the split their decode produces offset target = the residual the pooled centroid leaves (±½ cell), not the position that centroid already encodes deployed graph byte-identical to the previous experiment — only the training targets changed The champion, drawn by the design agent before it was allowed to train. One camera crop enters at the left; the trunk squeezes it to a short vector; the wide fan is the choice over all 2,970 map tiles; the small head after it is the nudge inside the winning tile. Grey is the harness's frozen contract, red is what this experiment changed , and the ochre lane at the bottom is training-only scaffolding that never boards the aircraft. Click to enlarge, or see every proposal on the model designs page.
Ask a network for latitude and longitude and it fails badly: when it is
unsure it has no way to say so, and hedging toward the middle of the map is
what keeps its average error down. Our first attempt was confident on every
frame and wrong on every frame.
So we stopped asking for a coordinate and asked a multiple-choice
question instead. Berlin is cut into 2,970 tiles of 128 m and
the network points at one. Now the answer carries its own certainty: sure,
and the belief piles onto a single tile; lost, and it smears across the city
— and the UAV says nothing instead. A second output nudges the fix to
a precise point inside the chosen tile.
The result is one file of 3.1 MB that answers in
1.6 ms . Nothing is looked up, nothing is matched
— the map is the weights. Each refinement on the way is its own
experiment in the research log , with the
figures on the model designs
page.
One model per area, by design. Asked about a block of Berlin held
out of training entirely, the same model returns 0% usable fixes
— the map lives in the weights, so ground it was never shown is ground
it cannot place. The pipeline takes any bounding box; each trained model
knows only its own. Night flying is scoped out for now: everything here is
raw daytime imagery.
tile 280 of 2,970 52.52377 N · 13.32644 Etile 316 of 2,970 52.52471 N · 13.39433 Etile 1,059 of 2,970 52.50790 N · 13.34402 Etile 1,086 of 2,970 52.50861 N · 13.39492 Etile 1,792 of 2,970 52.49342 N · 13.37850 Etile 2,443 of 2,970 52.47938 N · 13.36205 Etile 2,562 of 2,970 52.47732 N · 13.37909 Etile 2,571 of 2,970 52.47755 N · 13.39605 EEight of the 2,970 answers. Every tile is 128 m of real Berlin and owns one coordinate, so “which tile is this?” is the same question as “where am I?” — a river bend is easy; the empty field at the end is why the model must also know when not to answer.
3
How our research works The model was found, not designed — by a loop of coding agents.
INFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 camera frame one daytime frame frozen contract the experiment goes here architecture · feature extraction · decode · confidence — the agent may redraw all of it each figure below is one proposal for the inside of this box whatever fills the box must export to one ONNX file ≤ 4 MiB and answer in ≤ 250 ms frozen contract (lat, lon, confidence) position fix + confidence losses · supervision targets · samplers scaffolding that shapes the weights during training — torn down before flight, never in the exported model The search space. The gray endpoints are frozen
— one camera crop in, one (lat, lon, confidence)
answer out — and the dashed box is everything an experiment may
change. Every design in the
model designs gallery is one way
of filling it.
No human designed the champion. A
Karpathy-style
autoresearch loop did: each round, a coding agent reads the full
experiment history, proposes one focused change, and pre-registers
its hypothesis and expected outcome before any code is written. The harness
trains it, scores it, and keeps it only if it beats the champion.
Two roles, split clean. The agents own the experiments. The
frozen harness owns the exam: the data pipeline, the held-out
viewpoints and the scorer are read-only, so the loop can rewrite the model
but never the ruler it is measured by. We supervise from outside
— set the levers, curate the inspiration, and decide when an era is
over.
THE LOOP
experiment after experiment
PROPOSE
one focused change
1
2
TRAIN
on the whole city
3
SCORE
the mission penalty
4
KEEP
only if it beats the champion
HUMAN SUPERVISOR
sets the levers,
curates the inspiration
FROZEN RULER
pipeline, eval set, scorer —
read-only to the agents
Each round keeps only what beats the best so far, and
the next experiment builds on it.
Pre-registered, one change at a time
Hypothesis, method and expected outcome are written down before
implementation, and each experiment makes exactly one focused change —
so every verdict in the record says something.
Pivoting on patience
A run has a patience : spend it on reverted experiments and the
next design must abandon the current backbone for a genuinely different one
— enforced by reading the resulting source, not by asking nicely.
A frozen ruler
The score is the product requirement — usable fixes minus
confidently-wrong ones — and the agent cannot touch it. When a score
and the product disagree, the ruler gets fixed and the lineage is re-measured,
never quietly erased.
Everything is recorded
Every experiment is a git commit, a database row and a gallery entry
— kept and reverted alike. The
lineage , evolution graph
and lab notebook are rendered from
that record, not written after the fact.
Our model designs
Every experiment's model design, drawn by the design agent itself
before it was allowed to train — camera frame entering on the
left, (lat, lon, confidence) leaving on the right, red marking what that
experiment changed. All fifty-nine that carry a figure, kept and reverted
alike; the model designs page
has each at full size:
INFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 camera frame one daytime frame frozen contract convolutional encoder Conv 3×3, stride 2 ×4 · BN + ReLU 64²×32 8²×160 160-d GAP FC → 2,970 cell logits softmax probability field 55×54 map cells · 128 m each 3×3 pooled centroid mass-weighted balance point of the winning cell + 8 neighbours within-cell offset tanh · ½ cell frozen contract (lat, lon, confidence) position fix + confidence confidence 0–1 = pooled mass over the whole 3×3 block may abstain instead of guessing soft cross-entropy vs a bilinear tent over the 4 nearest cell centres — border views taught the split their decode produces offset target = the residual the pooled centroid leaves (±½ cell), not the position that centroid already encodes deployed graph byte-identical to Fig. 4 — only the training targets changed 5.5 · champion · Decode-consistent training targetsINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 camera frame one daytime frame frozen contract convolutional encoder Conv 3×3, stride 2 ×4 · BN + ReLU 64²×32 8²×160 160-d GAP FC → 2,970 cell logits softmax probability field 55×54 map cells · 128 m each 3×3 pooled centroid mass-weighted balance point of the winning cell + 8 neighbours within-cell offset tanh · ½ cell frozen contract (lat, lon, confidence) position fix + confidence confidence 0–1 = pooled mass over the whole 3×3 block may abstain instead of guessing cross-entropy on the one true cell + smooth-L1 offset — unchanged from Fig. 3 full-lattice coverage, fresh rotations, cosine LR — byte-identical to Fig. 3; only the deployed decode moved 5.4 · kept · Local belief pooling decodeINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 camera frame one daytime frame frozen contract convolutional encoder Conv 3×3, stride 2 ×4 · BN + ReLU 64²×32 8²×160 160-d GAP FC → 2,970 cell logits softmax probability field 55×54 map cells · 128 m each argmax over 2,970 cells one winning cell → its centre, nudged by the offset within-cell offset tanh · ½ cell frozen contract (lat, lon, confidence) position fix + confidence confidence 0–1 = softmax mass on the winning cell may abstain instead of guessing cross-entropy + offset loss unchanged — but every epoch is now one FULL pass over all 72,712 lattice positions 581,696 views, ~196 per cell (12× Fig. 2) · fresh rotation per position per epoch · cosine LR 1e-3 → 1e-4 architecture restored verbatim from Fig. 2 — the training schedule is the change 5.3 · kept · Full-lattice coverage training for the map-cell classifierINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 camera frame one daytime frame frozen contract convolutional encoder Conv 3×3, stride 2 ×4 · BN + ReLU 64²×32 8²×160 160-d GAP FC → 2,970 cell logits softmax probability field 55×54 map cells · 128 m each argmax over 2,970 cells one winning cell → its centre, nudged by the offset within-cell offset tanh · ½ cell frozen contract (lat, lon, confidence) position fix + confidence confidence 0–1 = softmax mass on the winning cell may abstain instead of guessing cross-entropy on the true cell (label smoothing 0.05) + smooth-L1 on the within-cell offset 6,000 of the 72,712 lattice positions redrawn each epoch with fresh headings — 48,000 views for 2,970 cells (~16 each) 5.2 · reverted · Map-cell classification decodeINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 camera frame one daytime frame frozen contract convolutional encoder Conv 3×3, stride 2 ×4 · BN + ReLU 64²×16 8²×128 128-d vector global avg pool FC 128 → 3 sigmoid one shot — no map structure frozen contract (lat, lon, confidence) position fix + confidence mean-squared error on (u, v) the whole map supervises one number pair BCE ×0.1 on confidence target: error < half the map extent starting line — the harness's own code, no design agent involved 5.1 · kept · Baseline seedINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 camera frame one daytime frame frozen contract convolutional encoder Conv 3×3, stride 2 ×4 · BN + ReLU 64²×16 8²×128 128-d vector global avg pool FC 128 → 1,024 cell logits + 1 confidence softmax probability field 1,024 map cells · 32×32 · 217 m each argmax over 1,024 cells one winning cell → its centre, nudged by its own offset within-cell offset FC 128 → 1,024×(du,dv) · tanh ±½ cell frozen contract (lat, lon, confidence) position fix + confidence BCE ×0.1 on confidence the baseline's free sigmoid, untouched cross-entropy on the true cell + MSE on its own offset teacher-forced; trunk, data and 8-epoch budget unchanged drawn identically on purpose — a faithful rebuild of the experiment whose source a bad metric deleted 4.4 · kept · Re-implementation of lost experiment 2 — classify-then-refineINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 camera frame one daytime frame frozen contract convolutional encoder Conv 3×3, stride 2 ×4 · BN + ReLU 64²×16 8²×128 128-d vector global avg pool FC 128 → 3 sigmoid one shot — no map structure frozen contract (lat, lon, confidence) position fix + confidence every presentation is now a different vantage at a fresh heading ~48,000 distinct lattice positions streamed at the same 48,000 gradient steps loss unchanged MSE on (u, v) + BCE ×0.1 on confidence the graph is the baseline's, untouched — the data diet is the whole experiment 4.3 · reverted · Coverage over repetitionINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 camera frame one daytime frame frozen contract convolutional encoder Conv 3×3, stride 2 ×4 · BN + ReLU 64²×16 8²×128 128-d vector global avg pool FC 128 → 1,024 cell logits + 1 confidence softmax probability field 1,024 map cells · 32×32 · 217 m each argmax over 1,024 cells one winning cell → its centre, nudged by its own offset within-cell offset FC 128 → 1,024×(du,dv) · tanh ±½ cell frozen contract (lat, lon, confidence) position fix + confidence BCE ×0.1 on confidence the baseline's free sigmoid, untouched cross-entropy on the true cell + MSE on its own offset teacher-forced; trunk, data and 8-epoch budget unchanged 4.2 · kept · Classify-then-refineINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 camera frame one daytime frame frozen contract convolutional encoder Conv 3×3, stride 2 ×4 · BN + ReLU 64²×16 8²×128 128-d vector global avg pool FC 128 → 3 sigmoid one shot — no map structure frozen contract (lat, lon, confidence) position fix + confidence mean-squared error on (u, v) the whole map supervises one number pair BCE ×0.1 on confidence target: error < half the map extent starting line - the same naive baseline, re-measured on the corrected viewpoint holdout 4.1 · kept · Baseline seed on the corrected viewpoint-holdout splitINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 camera frame one daytime frame frozen contract convolutional encoder Conv 3×3, stride 2 ×4 · BN + ReLU 64²×16 8²×128 128-d vector global avg pool FC 128 → 4,096 cell logits softmax probability field 4,096 map cells · 64×64 · 108 m each soft-argmax over 4,096 cells every cell centre, weighted by its probability frozen contract (lat, lon, confidence) position fix + confidence confidence 0–1 = heaviest 5×5-cell block of belief may abstain instead of guessing loss, head and schedule all unchanged — what moves is which 6,000 positions each pass is built from re-drawn at random from all ~45,000 mapped vantages before every pass, instead of one frozen 6,000 draw reused 96 times deployed graph byte-identical to the experiment above it — only the training diet changed 3.5 · kept · Per-epoch position resamplingINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 camera frame one daytime frame frozen contract convolutional encoder Conv 3×3, stride 2 ×4 · BN + ReLU 64²×16 8²×128 4×4×32 descriptor 1×1 conv 128→32 · 2×2 pool FC 512 → 256 cell logits softmax probability field 256 map cells · 16×16 · 434 m each posterior-weighted blend every cell's centre plus its own pointer, averaged by belief per-cell pointer FC 512 → 256×(du,dv) · tanh ±¾ cell frozen contract (lat, lon, confidence) position fix + confidence confidence 0–1 = heaviest 2×2-cell block of belief may abstain instead of guessing soft-label cross-entropy over the 256 coarse cells (σ = ½ cell) + MSE on the true cell's pointer + MSE on the blended (u, v) same 96-pass, fresh-heading schedule as the champion — the loss now grades 256 big squares and teaches a pointer inside each the descriptor moves with the decode: a pooled texture bag cannot say WHERE in a cell the frame sits 3.4 · reverted · Hierarchical decodeINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 camera frame one daytime frame frozen contract convolutional encoder Conv 3×3, stride 2 ×4 · BN + ReLU 64²×16 8²×128 128-d vector global avg pool FC 128 → 4,096 cell logits softmax probability field 4,096 map cells · 64×64 · 108 m each soft-argmax over 4,096 cells every cell centre, weighted by its probability frozen contract (lat, lon, confidence) position fix + confidence confidence 0–1 = heaviest 5×5-cell block of belief may abstain instead of guessing same cross-entropy and same soft labels — the study budget is the change: 96 passes, not 8 cosine LR 1e-3 → 1e-5 · every crop re-drawn at a fresh random heading before every pass the head is restored verbatim from the gated-out experiment above it — only the training schedule is new 3.3 · kept · Re-land the validated 64x64 grid-classification head with a convergence-scale training budget (x12 epochs, cosine LR, fresh per-epoch rotations)INFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 camera frame one daytime frame frozen contract convolutional encoder Conv 3×3, stride 2 ×4 · BN + ReLU 64²×16 8²×128 128-d vector global avg pool FC 128 → 4,096 cell logits softmax probability field 4,096 map cells · 64×64 · 108 m each soft-argmax over 4,096 cells every cell centre, weighted by its probability frozen contract (lat, lon, confidence) position fix + confidence confidence 0–1 = heaviest 5×5-cell block of belief may abstain instead of guessing cross-entropy against Gaussian-smoothed cell labels (σ = 1 cell) + a small L2 on the soft-argmax coordinate 6,000 crops, one frozen rotation each, 8 epochs — the same budget the naive baseline was given gated FAIL — confident on only 10.8% of frames, under the harness's 20% coverage floor 3.2 · reverted · Reparameterize localization as 64x64 map-grid classification with in-graph soft-argmaxINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 camera frame one daytime frame frozen contract convolutional encoder Conv 3×3, stride 2 ×4 · BN + ReLU 64²×16 8²×128 128-d vector global avg pool FC 128 → 3 sigmoid one shot — no map structure frozen contract (lat, lon, confidence) position fix + confidence mean-squared error on (u, v) the whole map supervises one number pair BCE ×0.1 on confidence target: error < half the map extent starting line — the harness's own code re-run on Berlin alone, no design agent involved 3.1 · kept · Baseline seedINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 camera frame one night exposure frozen contract ×8 exact-pixel views (D4) rotations + mirror, shared weights SqueezeNet1.1 Fire trunk stem + 4 fire blocks (BSD-3) mobilenet_v3_small banned this round 63²×64 7²×384 lum FiLM (γ, β) MLP modulates feature map 384-d descriptor GAP, D4-pooled avg 1-ch layout 1×1 squeeze, 49-d flat single Linear → 1,024 gate + dual head deleted probability field 32×32 cells over the map adaptive-β commit frozen contract (lat, lon, confidence) position fix + confidence β = f(entropy, peak) learned per-example sharpening confidence 0–1 384-d GAP + adaptive β + peak/entropy/gap Gaussian-CE vs cell target (unchanged) σ = 1.5 cells over the 32×32 field L2 on decoded (u, v) expected-coordinate loss — now also trains the β gauge confidence BCE ×0.3 (unchanged loss) same hit-radius target, new head inputs 2.62 · reverted · Fire-module SqueezeNet1.1 trunk + FiLM-conditioned single field + D4 symmetry + adaptive-sharpening decode (forced pivot, mobilenet_v3_small banned)INFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 camera frame one night exposure frozen contract ×4 crop rotations (C4) 0/90/180/270°, shared weights depthwise-separable trunk from-scratch, 4× wider 64²×64 8²×320 320-d descriptor GAP, C4-pooled avg unified FC → 1600 logits QATLinear, weight fake-quant probability field 40×40 cells, finer than prior 32×32 sharpen β=3 → soft-argmax over the finer 1,600-cell grid frozen contract (lat, lon, confidence) position fix + confidence confidence 0–1 peak/entropy/gap of new field weight fake-quant every step round(w/scale)·scale — straight-through grad int8 export, post-training dynamic quantization — 1 B/weight, real rounding Gaussian-CE vs cell target (unchanged) σ = 1.5 cells, now over the 40×40 field L2 on decoded (u, v) expected-coordinate loss, unchanged confidence BCE ×0.3 unchanged hit-radius target 2.61 · reverted · Quantization-freed capacityINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 camera frame one night exposure frozen contract illumination-invariant channel log-luminance − own Gaussian blur (fixed math, no gradient) concat 128²×4 input ×4 crop rotations (C4) 0°/90°/180°/270°, shared weights from-scratch conv encoder Conv 3×3 s2 ×4 · BN+ReLU — pretrained trunk banned this round 128²×4 8²×128 128-d descriptor GAP, C4-pooled avg unified FC → 1024 logits gate + dark-expert head deleted probability field 32×32 cells, same layout-code concat sharpen β=3 → soft-argmax decode mechanism unchanged frozen contract (lat, lon, confidence) position fix + confidence confidence 0–1 peak/entropy/gap of new field same recipe, no gate-derived inputs left lighting-partner crop same spot & heading, other bucket train-only shared trunk f partner 2nd forward pass L2 consistency loss ×0.1 (new) same spot, 2 lighting buckets → match descriptors watch: across-location variance must stay healthy Gaussian-CE vs cell target (unchanged) σ = 1.5 cells, same GRID_K=32 field L2 on decoded (u, v) (unchanged) confidence BCE ×0.3 (unchanged loss) same hit-radius target, new field inputs 2.60 · reverted · Corrected exp-38 retryINFERENCE PATH — WHAT FLIES (both specialists always run; the gate blends, never branches) TRAINING — THREE PHASES, NEVER JOINT 128²×3 camera frame one of 6 lighting buckets frozen contract day-specialist trunk from-scratch · day buckets only day field + decode 16×16 grid, soft-argmax day (u,v,conf) 3 pixel stats mean · spread · dark% MLP(8) no trunk, no features gate g day-like ↔ night-like dispatcher night-specialist trunk independent copy, own weights · night buckets only night field + decode 16×16 grid, soft-argmax night (u,v,conf) blend g·day + (1−g)·night conf nudged down when g is unsure frozen contract (lat, lon, confidence) position fix + confidence phase 1 — day trunk+field+decode+conf fresh optimizer · Gaussian-CE + L2 + 0.3× conf BCE, day buckets only phase 2 — night trunk+field+decode+conf separate fresh optimizer · same loss · zero gradient crosses into phase 1 phase 3 — freeze both specialists train only the gate (~40 params) · Huber/L2 + 0.3× conf BCE, all 6 buckets confusability sampler runs twice, independently day draw and night draw never mix 2.59 · reverted · Independently-trained day/night specialist twins with a trunk-free brightness dispatcherINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — JOINT EVERY STEP, NEVER FLY 128²×3 camera frame one of 6 lighting buckets frozen contract rotation fan 4× C4 views, shared net convolutional encoder Conv 3×3, stride 2 ×4 · BN + ReLU 64²×16 8²×64 layout summary spatial code + texture avg FC → 1024 logits single unified scorer probability field 32×32 field, gate removed soft-argmax decode reads one unified field, sharpening unchanged frozen contract (lat, lon, confidence) position fix + confidence confidence 0–1 peak / entropy / gap + raw crop brightness (new) may abstain instead of guessing reconstruction decoder ConvTranspose 4×4 ×4 — reads row 0, un-rotated 8²×64 64²×16 repainted crop 128²×3, sigmoid discarded before export reference.tif twin same cx, cy, angle clean daytime, any bucket L1 pixel-reconstruction loss, weight 1.0 every crop, every bucket, every step — not a pretrain phase Gaussian-CE on field + L2 on (u,v) + BCE on confidence loss formula itself unchanged — only the field it grades is unified now one shared LR (1e-3) for all params from-scratch trunk — no pretrained-feature 10× discount to protect 2.43 · reverted · Joint reconstruction-canonicalization trunkINFERENCE PATH — WHAT FLIES PHASE 0 — SELF-SUPERVISED PRETRAINING (TRAIN SPLIT ONLY, RUNS FIRST) PHASE 2 — SUPERVISED TRAINING (UNCHANGED LOSS FORMULA, NEW SCHEDULE) 128²×3 camera frame one night exposure frozen contract C4 rotation fan shared net, feeds SSL views from-scratch conv trunk 4× conv3×3 s2 + BN + ReLU, no ImageNet weights 128²×3 8²×64 layout summary spatial code + texture avg FC → 1024 logits single unified scorer probability field 32×32 field, gate removed soft-argmax decode answer = Σ probability · cell-center confidence 0–1 peak/entropy/gap + brightness (new) may abstain instead of guessing frozen contract (lat, lon, confidence) position fix + confidence view A lighting/heading draw 1 view B lighting/heading draw 2 same physical location — uniform random draw, 128 locs/step shared trunk same weights as above proj head 64→128→64, L2-norm in loss same place → pull together other batch places → push apart NT-Xent (InfoNCE) temperature 0.1, batch 128 schedule: 150 contrastive steps (3×50), 128 locs/step, then usual supervised epochs (count unchanged) trunk LR gap to head relaxed 10× → 2× (5e-4 vs 1e-3) — pretrained trunk needs less protection Gaussian-CE on field + L2 on decoded (u,v) — loss formula itself unchanged confusability-weighted sampler (exp 35) still feeds THIS stage only, not pretraining 2.42 · reverted · Domain-native contrastive pretraining replaces the ImageNet trunk (corrected re-attempt)INFERENCE PATH — WHAT FLIES PHASE 0 — SELF-SUPERVISED PRETRAINING (TRAIN SPLIT ONLY, RUNS FIRST) PHASE 2 — SUPERVISED TRAINING (UNCHANGED LOSS FORMULA, NEW SCHEDULE) 128²×3 camera frame one night exposure frozen contract C4 rotation fan shared net, feeds SSL views from-scratch conv trunk 4× conv3×3 s2 + BN + GELU, no ImageNet weights 128²×3 8²×64 layout summary spatial code + texture avg FC → 1024 logits single unified scorer probability field 32×32 field, gate removed learned β softplus, init 3.0 soft-argmax decode answer = Σ probability · cell-center confidence 0–1 peak/entropy/gap + brightness (new) may abstain instead of guessing frozen contract (lat, lon, confidence) position fix + confidence view A lighting/heading draw 1 view B lighting/heading draw 2 same physical location — confusability-weighted sampler (exp 35) shared trunk same weights as above proj head 64→64→32, L2-norm same place → pull together other batch places → push apart NT-Xent (InfoNCE) temperature 0.15, batch 128 schedule: 3 SSL epochs (cosine→0), then 24 supervised epochs (count unchanged) trunk/head LR ratio relaxed 10× → 3.3× (3e-4 vs 1e-3) — domain prior needs less protection Gaussian-CE on field + L2 on decoded (u,v) — loss formula itself unchanged same confusability-weighted sampler (exp 35) feeds this stage too 2.41 · reverted · Domain-native contrastive pretraining replaces the ImageNet trunkINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 camera frame one night exposure frozen contract rotation fan ×4 turns, C4 vote feature extractor MobileNetV3-S, 2 taps ↓ stride-8 tap feeds the fine field below coarse localization field 8×8 cells ≈875 m, shared 1×1 conv fine offset field per-cell (Δu,Δv,logσ), ONE shared head decode β-sharpened mean of (cell center + own offset) frozen contract (lat, lon, confidence) position fix + confidence confidence 0–1 peak/entropy shape + fine field's own logσ — may abstain coarse Gaussian-CE over 8×8 cells (was 32×32) gradient now shared through ~100 conv weights, not 1,024 free rows + fine offset Huber loss, gathered at the TRUE cell only unimodal regression — coarse stage already picked the place + L2 on final decode + conf BCE training data: confusability sampler (exp 35) recomputed at the new 8×8 granularity schedule: 2-epoch warmup, fresh offset head 2.40 · reverted · Coarse-to-fine geolocalizationINFERENCE PATH — WHAT FLIES 128²×3 Camera frame one of 6 lighting renders frozen contract 4×128²×3 Rotation fan votes the new similarity map 64²×16 8²×48 Feature extractor MobileNetV3-Small, +NEW stride-8 tap 16²×24 skip GAP · 24-d NEW tap GAP 48 + layout 512 kept descriptor pair Crop embedding 64-d, L2-normalized — NEW lighting FiLM γ,β from brightness+GAP Probability map cosine sim. × cells, C4-avg Fourier(u, v) shared 2-layer MLP generates all 1,024 cell embeddings — not 1,024 free rows Decode β-sharpened soft-argmax (kept form) frozen contract (lat, lon, confidence) position fix + confidence confidence 0–1 peak-vs-2nd-peak margin — NEW training-only — never flies Gaussian-bump CE on the temperature-scaled cosine-sim field (kept CE form; logits are bounded sim × learned scale, not raw FC output) + coord L2 + conf BCE (kept, unchanged math) — training data/schedule below kept, unchanged: confusability-weighted location sampler (rd 35) kept, unchanged: 3 seeded relight realizations per bucket (rd 17) kept, unchanged: 24-pass fresh-draw epochs, cosine LR glide to zero (rd 25) removed: dark-expert head + brightness gate (rd 12) — folded into FiLM above 2.39 · reverted · Coordinate-generated map fieldINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 camera frame one night exposure frozen contract 6-ch input RGB ⊕ retinex illumination-invariant channel log(I) − large-σ Gaussian blur, fixed Rotation fan 4×90° turns, 6ch MobileNetV3-Small trunk (9 blocks) pretrained · stem widened, rest frozen 64²×6 8²×48 stem re-init 3→6ch RGB weights kept + 0.1× copy 48-d GAP texture avg, unchanged layout squeeze 1×1 conv, 8ch 512-d layout code NEW: L2-norm per cell probability map gated 2-expert blend, unchanged β-sharpened decode C4-voted, unchanged frozen contract (lat, lon, confidence) position fix + confidence confidence: field-shape gauge, unchanged training data: confusability draw (kept, exp 35) + NEW per-crop sharpness jitter 50% chance Gaussian blur (σ 0.3–1.5px) else unsharp-mask — the blur-riding shortcut can no longer carry location signal training schedule: NEW 5%-of-steps linear warm-up before the exp-25 cosine decay protects the freshly re-initialized stem weights from early large gradients training signal: Gaussian-CE + coord L2 + conf BCE unchanged 2.38 · reverted · Illumination-invariant retinex channel joins raw RGB from the pixel level through the trunkCamera frame 128²×3 · ~1 m/px frozen contract C4 rotation fan 4 exact 90° turns 4×128²×3 64²×16 16²×24 8²×48 MobileNetV3-Small trunk pretrained, gently fine-tuned GAP 48 layout 512 Multi-hypothesis head 8 learned points, softmax weight no separate expert 4-view weighted vote weight-argmax point per heading, softmax-blended conf: 4-view agreement × winning weight (lat, lon, confidence) frozen contract training-only — never flies cell-descriptor map from reference.tif, offline, once per area confusability-weighted sampling far, look-alike cells drawn ~2x more often per epoch half the draw stays uniform — full coverage kept (kept · rd 35) still 6,000 places/bucket/epoch, fresh-drawn (rd 20 · reweighted only) 3 seeded relight realizations per bucket (kept · rd 17) 24 passes · cosine LR glide to zero (kept · rd 25) + winner-take-all: nearest of 8 hyps gets the L2 grad, others ε=0.05 (NEW) weight head trained by cross-entropy vs. the nearest-hypothesis index (8-way, not 1024-way) winner-take-all regression nearest hyp wins the gradient; rest share ε=0.05 2.37 · reverted · Multi-hypothesis coordinate regression replaces the 1024-cell fieldCamera frame 128²×3 · ~1 m/px frozen contract C4 rotation fan 4 exact 90° turns 4×128²×3 64²×16 16²×24 8²×48 MobileNetV3-Small trunk pretrained, gently fine-tuned GAP 48 layout 512 lighting gate brightness-blended pair of scorers σ probability field 32×32 cells, C4-vote averaged committed decode β-sharpened soft-argmax (unchanged) conf: field-shape hit predictor (lat, lon, confidence) frozen contract training-only — never flies cell-descriptor map from reference.tif, offline, once per area confusability-weighted sampling far, look-alike cells drawn ~2x more often per epoch half the draw stays uniform — full coverage kept (kept · rd 35) still 6,000 places/bucket/epoch, fresh-drawn (rd 20 · reweighted only) 3 seeded relight realizations per bucket (kept · rd 17) 24 passes · cosine LR glide to zero (kept · rd 25) + hardest-impostor margin hinge on C4-voted logits (NEW) hardest-impostor margin hinge s_neg (Cheb>3) pushed below s_pos (Cheb≤1) 2.36 · reverted · Hardest-impostor margin hinge, retested on the confusability-weighted championCamera frame 128²×3 · ~1 m/px frozen contract C4 rotation fan 4 exact 90° turns 4×128²×3 64²×16 16²×24 8²×48 MobileNetV3-Small trunk pretrained, gently fine-tuned GAP 48 layout 512 lighting gate brightness-blended pair of scorers σ probability field 32×32 cells, C4-vote averaged committed decode β-sharpened soft-argmax (unchanged) conf: field-shape hit predictor (lat, lon, confidence) frozen contract training-only — never flies cell-descriptor map from reference.tif, offline, once per area NEW — confusability-weighted sampling far, look-alike cells drawn ~2x more often per epoch half the draw stays uniform — full-map coverage kept still 6,000 places/bucket/epoch, fresh-drawn (rd 20 · reweighted only) 3 seeded relight realizations per bucket (kept · rd 17) 24 passes · cosine LR glide to zero (kept · rd 25) Gaussian-bump CE + coord L2 + conf BCE (kept · unchanged) 2.35 · kept · Confusability-weighted location samplingCamera frame 128²×3 · ~1 m/px frozen contract C4 rotation fan 4 exact 90° turns 4×128²×3 64²×16 16²×24 8²×48 MobileNetV3-Small trunk pretrained, gently fine-tuned GAP 48 layout 512 lighting gate brightness-blended pair of scorers σ probability field 32×32 cells, C4-vote averaged committed decode β-sharpened soft-argmax (unchanged) conf: field-shape hit predictor (lat, lon, confidence) frozen contract training-only — never flies fresh 6,000-place draw per bucket per epoch (kept · rd 20) 3 seeded relight realizations per bucket (kept · rd 17) 24 passes · cosine LR glide to zero (kept · rd 25) Gaussian-bump CE + coord L2 + conf BCE (kept · unchanged) NEW — hardest-impostor margin · λ 0.5 best-scoring cell outside the 7×7 ring around truth must trail the 3×3 truth block by ≥ 1.0 logit truth 3×3 hardest impostor 2.34 · reverted · Hardest-impostor margin lossCamera frame 128²×3 · ~1 m/px frozen contract C4 rotation fan 4 exact 90° turns 4×128²×3 64²×16 16²×24 8²×48 MobileNetV3-Small trunk pretrained, gently fine-tuned GAP 48 layout 512 lighting gate brightness-blended pair of scorers σ probability field 32×32 cells, C4-vote averaged committed decode β-sharpened soft-argmax (unchanged) conf: field-shape hit predictor (lat, lon, confidence) frozen contract training-only — never flies crisp as today defocused NEW — per-crop sharpness roll ¼ pin-sharp (no resample) · ½ extra defocus σ∈U(0.4,1.4) px fresh 6,000-place draw per bucket per epoch (kept · rd 20) 3 seeded relight realizations per bucket (kept · rd 17) 24 passes · cosine LR glide to zero (kept · rd 25) Gaussian-bump CE + coord L2 + conf BCE 2.33 · reverted · Sensor-sharpness nuisance randomizationINFERENCE PATH — WHAT FLIES TRAINING-TIME CALIBRATION — NEVER FLIES 128²×3 camera frame one of 6 lighting renders frozen contract rotation fan four 90° turns MobileNetV3-Small trunk pretrained, 9 blocks 8²×48 crop descriptor 512-d layout + 48-d GAP probability field 32×32 cells · two experts · C4 vote β-sharpened decode — balance point of the hottest mode frozen contract (lat, lon, confidence) position fix + confidence field-shape statistics peak · entropy · decode gap self-trust score z − regime abstention bar (interpolated) confidence 0–1 conf < 0.3 → 'no fix' (must answer ≥20%) frame brightness six regime anchors abstention bar per regime, blended by frame brightness fenced calibration blocks ~10% of train blocks by stable hash — excluded from every training draw, same halo rule as eval blocks (grey); errors are measurable there, terrain is honestly unseen median error q* ≥ 0.26 keep rate per-regime operating point each lighting regime's keep rate picked at the low point of its fenced risk–coverage curve (floor 0.32) — the six thresholds become the anchor row above 2.32 · reverted · Risk-controlled abstentionINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 camera frame one of 6 renders frozen contract rotation fan 4 turns, voted at the field 64²×16 8²×48 MobileNetV3-Small trunk pretrained, gently fine-tuned 4×4×8 crop fingerprint 1×1 conv + 2×2 pool · each tap = 32 m of ground bright/dark variants blended by the kept gate slide across the map 8 ch × ~217×~213 learned neural map 32 m per cell · lives in the weights match-score field ~45,000 positions · softmax 32 m pitch — was 217 m sharpened soft-argmax commit to the hottest mode (unchanged) frozen contract (lat, lon, confidence) position fix + confidence confidence 0–1 reads the map's shape — may abstain Gaussian-CE + decode L2 + conf BCE — unchanged same σ (≈330 m tolerance) in map units, graded on the ~45k-position field training data, sampler & 24-epoch cosine schedule unchanged 2.31 · reverted · Neural-map correlation fieldINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 camera frame one of 6 renders frozen contract C4 rotation fan 4 turns, logits voted 64²×16 8²×48 MobileNetV3-Small trunk ImageNet-pretrained, blocks 0–8 8×8 layout squeeze 1×1 conv → 512-d GAP 48-d 560-d 560-d descriptor 48 texture ⊕ 512 layout day specialist FC 560 → 1,024 each dusk specialist night specialist × w₁ × w₂ × w₃ lighting dispatcher softmax → w₁,w₂,w₃ crop brightness probability field 32×32 cells · C4 logit vote β-sharpened soft-argmax commits to the dominant mode frozen contract (lat, lon, confidence) position fix + confidence confidence 0–1 may abstain instead of guessing cross-entropy on the routing weights vs the TRUE lighting bucket buckets group as day / dusk / night — the labels are free at train time specialists' FC weights ride int8 in the exported ONNX export-only pack — ~7 m cost measured (exps 26/29); 3 full heads fit the 4 MiB gate Gaussian-CE + decode L2 + conf BCE — unchanged training data & schedule also unchanged (24 fresh-draw passes) 2.30 · reverted · Supervised lighting dispatcherINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 camera frame one of 6 lighting renders frozen contract C4 rotation fan 4 turns, logits averaged 64²×16 8²×48 16²×24 MobileNetV3-Small trunk ImageNet-pretrained blocks 0–8 — unchanged 1×1 conv 24 → 4 ch · flatten → 1,024-d GAP · 1×1 layout squeeze crop summary — 1,584-d 48-d GAP + 512-d 8×8 layout (as before) + 1,024-d stride-8 fine code — new night gate blends a 48-d dark scorer per crop (unchanged) FC 1,584 → 1,024 logits weight tables fly int8 — measured ±7 m probability field 32×32 cells · C4 logit vote (unchanged) β-sharpened soft-argmax commit to the dominant mode (unchanged) frozen contract (lat, lon, confidence) position fix + confidence confidence 0–1 reads its own field shape Gaussian-CE (σ=1.5 cells) + decode-L2 + conf-BCE — unchanged training procedure unchanged — 24 fresh-draw epochs, cosine LR → 0 2.29 · reverted · Stride-8 fine-layout tapCamera frame 128²×3, ~1 m/px frozen contract Rotation fan four 90° turns ×4 stem + early 64²×16 mid blocks 16²×24 blocks 4–8 8²×48 blocks 9–10 — reinstated dilated — 8²×96 layout code 608-d int8 FC store — reinstated quantized at export · ~7 m effect σ lighting gate Probability map 32×32 cells, C4 vote Decode sharpened balance point Confidence abstains on smeared maps (lat, lon, confidence) frozen contract abstain bar set on held-back ground — new ~10% of train blocks never enter a training draw; the bar is tuned on ground as unseen as the exam's training-only lane — losses and schedule unchanged: Gaussian-CE map target · committed-coord L2 · conf BCE · 24 fresh-draw epochs, cosine LR → 0 2.28 · reverted · Rerun, never scoredCamera frame 128²×3, ~1 m/px frozen contract Rotation fan four 90° turns ×4 stem + early 64²×16 mid blocks 16²×24 blocks 4–8 8²×48 blocks 9–10 — reinstated dilated — 8²×96 layout code 608-d int8 FC store — reinstated quantized at export · ~7 m effect σ lighting gate Probability map 32×32 cells, C4 vote Decode sharpened balance point Confidence abstains on smeared maps (lat, lon, confidence) frozen contract abstain bar set on held-back ground — new ~10% of train blocks never enter a training draw; the bar is tuned on ground as unseen as the exam's training-only lane — losses and schedule unchanged: Gaussian-CE map target · committed-coord L2 · conf BCE · 24 fresh-draw epochs, cosine LR → 0 2.27 · reverted · Unseen-ground confidence calibration unlocks the reinstated 3×-capacity trunkCamera frame 128²×3, ~1 m/px frozen contract Rotation fan four 90° turns ×4 stem + early 64²×16 mid blocks 16²×24 blocks 4–8 8²×48 blocks 9–10 — new dilated — 8²×96 layout code 608-d int8 FC store — new quantized at export · ~7 m effect σ lighting gate Probability map 32×32 cells, C4 vote Decode sharpened balance point Confidence abstains on smeared maps (lat, lon, confidence) frozen contract training-only lane — unchanged: Gaussian-CE map target · committed-coord L2 · conf BCE · 24 fresh-draw epochs, cosine LR → 0 2.26 · reverted · Deployment-envelope capacity scalingINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 · ~1 m/px camera frame one of six lighting renders frozen contract shared weights ×4 rotation fan 0°·90°·180°·270° — exact pixel turns MobileNetV3-Small trunk ImageNet init (BSD-3) · gently fine-tuned 8²×48 ×4 1024 logits ×4 four field maps same gated two-expert head reads each turn 32×32 cells voted probability field mean of four logit maps = geometric-mean vote β-sharpened soft-argmax commits to the hottest mode (unchanged) frozen contract (lat, lon, confidence) position fix + confidence confidence 0–1 reads the voted map’s shape (unchanged) may abstain position + confidence losses — all unchanged Gaussian-CE + decode L2 + conf BCE — still graded at the true centre epoch 1 epoch 2 … epoch 24 a fresh draw of 6,000 locations per bucket, every epoch every epoch, training restarts on a freshly drawn set of places LR before: constant LR, hard stop at epoch 8 now: cosine glide to zero across a 3× longer run convergence-scaled training — 3× the steps, annealed to zero The fresh-draw sampler (kept, left) made memorizing crops useless — but the schedule still stops at 8 passes, loss still falling, LR never lowered. A probe shows pure underfit: train crops now localize no better than eval crops (~1 km both). This run trains 24 fresh-draw passes with the LR gliding down a cosine — the same sampler, finally trained to converge. 2.25 · kept · Convergence-scaled trainingINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 · ~1 m/px camera frame one of six lighting renders frozen contract shared weights ×4 rotation fan 0°·90°·180°·270° — exact pixel turns MobileNetV3-Small trunk ImageNet init (BSD-3) · gently fine-tuned 8²×48 ×4 1024 logits ×4 four field maps same gated two-expert head reads each turn 32×32 cells voted probability field mean of four logit maps = geometric-mean vote β-sharpened soft-argmax commits to the hottest mode (unchanged) frozen contract (lat, lon, confidence) position fix + confidence confidence 0–1 reads the voted map’s shape (unchanged) may abstain position + confidence losses — all unchanged Gaussian-CE + decode L2 + conf BCE — still graded at the true centre epoch 1 epoch 2 … epoch 8 a fresh draw of 6,000 locations per bucket, every epoch every epoch, training restarts on a freshly drawn set of places kept from exp 20 — unchanged trunk feature grid 8²×48 identity turn · read before pooling 16² 32² 64² throwaway upsampling decoder 8²→64² · ~55k params · never exported 64²×3 redrawn daytime view from features alone 64²×3 daytime reference crop same spot & heading, free from sim L1 ×0.3 NEW — daytime-redraw auxiliary from its own features, the net must redraw this spot in clean daylight; all six renders share one daytime answer, so seeing through grain and darkness is the cheapest fit — decoder never flies 2.24 · reverted · Daytime-redraw auxiliary, rerunINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 · ~1 m/px camera frame one of six lighting renders frozen contract shared weights ×4 rotation fan 0°·90°·180°·270° — exact pixel turns MobileNetV3-Small trunk ImageNet init (BSD-3) · gently fine-tuned 8²×48 ×4 1024 logits ×4 four field maps same gated two-expert head reads each turn 32×32 cells voted probability field mean of four logit maps = geometric-mean vote β-sharpened soft-argmax commits to the hottest mode (unchanged) frozen contract (lat, lon, confidence) position fix + confidence confidence 0–1 reads the voted map’s shape (unchanged) may abstain position + confidence losses — all unchanged Gaussian-CE + decode L2 + conf BCE — still graded at the true centre epoch 1 epoch 2 … epoch 8 a fresh draw of 6,000 locations per bucket, every epoch every epoch, training restarts on a freshly drawn set of places kept from exp 20 — unchanged trunk feature grid 8²×48 identity turn · read before pooling 16² 32² 64² throwaway upsampling decoder 8²→64² · ~55k params · never exported 64²×3 redrawn daytime view from features alone 64²×3 daytime reference crop same spot & heading, free from sim L1 ×0.3 NEW — daytime-redraw auxiliary from its own features, the net must redraw this spot in clean daylight; all six renders share one daytime answer, so seeing through grain and darkness is the cheapest fit — decoder never flies 2.23 · reverted · Daytime-redraw auxiliaryINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 · ~1 m/px camera frame one of six lighting renders frozen contract shared weights ×4 rotation fan 0°·90°·180°·270° — exact pixel turns MobileNetV3-Small trunk ImageNet init (BSD-3) · gently fine-tuned 8²×48 ×4 1024 logits ×4 four field maps same gated two-expert head reads each turn 32×32 cells voted probability field mean of four logit maps = geometric-mean vote β-sharpened soft-argmax commits to the hottest mode (unchanged) frozen contract (lat, lon, confidence) position fix + confidence confidence 0–1 reads the voted map’s shape (unchanged) may abstain position + confidence losses — all unchanged Gaussian-CE + decode L2 + conf BCE — still graded at the true centre epoch 1 epoch 2 … epoch 8 a fresh draw of 6,000 locations per bucket, every epoch every epoch, training restarts on a freshly drawn set of places kept from exp 20 — unchanged trunk feature grid 8²×48 identity turn · read before pooling 64 per-patch map fields — 3 of 64 shown one shared 1×1 conv 48→1024 · training-only, deleted before export NEW — per-patch place supervision every feature cell must place ITS OWN 16 m patch of ground on the map, graded by a Gaussian bump at that patch's true spot = crop centre + rotated cell offset. 64 graded answers per crop instead of 1. The flying network is untouched — the aux head never ships. 2.22 · reverted · Train-only per-patch place supervisionINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 · ~1 m/px camera frame one of six lighting renders frozen contract shared weights ×4 rotation fan 0°·90°·180°·270° — exact pixel turns MobileNetV3-Small trunk ImageNet init (BSD-3) · gently fine-tuned 8²×48 ×4 1024 logits ×4 four field maps same gated two-expert head reads each turn 32×32 cells voted probability field mean of four logit maps = geometric-mean vote β-sharpened soft-argmax commits to the hottest mode (unchanged) frozen contract (lat, lon, confidence) position fix + confidence confidence 0–1 reads the voted map’s shape (unchanged) may abstain position + confidence losses — all unchanged Gaussian-CE + decode L2 + conf BCE — still graded at the true centre epoch 1 epoch 2 … epoch 8 a fresh draw of 6,000 locations per bucket, every epoch every epoch, training restarts on a freshly drawn set of places per-epoch location resampling — same training budget, 8× the places seen the last kept model studied the SAME 36,000 crops for all 8 epochs — same places, same headings, same simulator rolls — so memorizing each crop was the easiest way down the training loss. Now every epoch redraws its 6,000 locations per bucket (fresh headings and render mix): ~30k of the 45k train places get seen, each only ~1–2×. Memorizing single crops stops paying; only cues that transfer between places do — the skill an eval crop over never-trained terrain actually demands. 2.20 · kept · Per-epoch training-set resamplingINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 · ~1 m/px camera frame one of six lighting renders frozen contract shared weights ×4 rotation fan 0°·90°·180°·270° — exact pixel turns MobileNetV3-Small trunk ImageNet init (BSD-3) · gently fine-tuned 8²×48 ×4 1024 logits ×4 four field maps same gated two-expert head reads each turn 32×32 cells voted probability field mean of four logit maps = geometric-mean vote β-sharpened soft-argmax commits to the hottest mode (unchanged) frozen contract (lat, lon, confidence) position fix + confidence confidence 0–1 reads the voted map’s shape (unchanged) may abstain position + confidence losses — all unchanged Gaussian-CE + decode L2 + conf BCE — still graded at the true centre half of each bucket’s training crops are patched before batching crop from elsewhere same lighting bucket lift a 48–96 px block patched training crop label: still the true centre off-site distractor patching — p = 0.5, training only eval crops sit on never-trained blocks: the centre terrain is always unfamiliar, familiar terrain pokes in from an edge (~76% of eval crops contain some trained pixels). The probe behind exp 18: train-split crops localize to 267–400 m, eval-split to 708–1153 m — a ~3× structural gap, in every bucket. Training crops were always 100% familiar, so the whole-crop head never practised partial recognition; patched crops force it to answer from the content consistent with one place and down-weight the rest. 2.19 · reverted · Off-site distractor patchingINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 camera frame one of six lighting renders frozen contract rotation fan 0°·90°·180°·270° shared weights ×4 MobileNetV3-Small trunk ImageNet init (BSD-3) 8²×48 ×4 patch voters every cell = one voter 64 per-patch field votes shared 1×1 MLP 48→128→1024 — one field per patch ⋯ ×64 votes, each × learned weight wᵖ (softmax over patches) voted probability field weighted blend of 64 patch votes mean over 4 turns (unchanged) β-sharpened soft-argmax commits to the hottest mode (unchanged) frozen contract (lat, lon, confidence) position fix + confidence confidence 0–1 reads the voted map's shape (unchanged) may abstain dense per-patch supervision — 64 graded answers per crop each patch's vote is graded against a Gaussian bump on the map cell of ITS OWN ground content, computed exactly at train time as crop center + R(heading)·patch offset — a place can no longer be recognized by one whole-crop gestalt: all 64 voters must independently know where their ground sits probe, midday, matched headings: train-crop median 267–400 m vs held-out 708–1153 m — the global template head memorizes but does not generalize fused-field Gaussian-CE + decode L2 + conf BCE — unchanged · three-realization training renders (exp 17) — unchanged · calibration unchanged 2.18 · reverted · Dense per-patch field votingINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 camera frame one of six lighting renders frozen contract rotation fan 0°·90°·180°·270° — exact pixel turns shared weights ×4 MobileNetV3-Small trunk ImageNet init (BSD-3) · gently fine-tuned 8²×48 ×4 four field maps same gated two-expert head reads each turn 32×32 cells voted probability field mean of four logit maps = geometric-mean vote β-sharpened soft-argmax commits to the hottest mode (unchanged) frozen contract (lat, lon, confidence) position fix + confidence confidence 0–1 reads the voted map's shape (unchanged) may abstain Gaussian-CE + decode L2 + conf BCE — all unchanged the losses now grade the voted answer — train what flies seed₀ = the eval render seed₁ fresh dice roll seed₂ fresh dice roll each training crop is drawn from one of three seeded realizations nuisance-randomized training renders the frozen relight sim rolls dice for sensor noise + lamp thinning; the stored night render freezes ONE roll, ≈ half that image's content (mean |Δpx| 29/255 between rolls vs 6/255 at midday; render mean 60/255) → the 1024-way head memorizes roll-specific texture that cannot transfer to held-out locations. Training now draws each bucket's crops from three seeded rolls (⅓ each), so only the seed-stable structure — roads, buildings, where the lit areas sit — stays discriminative between locations. eval renders, model, losses, decode, calibration: all unchanged — train-time only 2.17 · kept · Nuisance-randomized training rendersINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 camera frame one of six lighting renders frozen contract rotation fan 0°·90°·180°·270° — exact pixel turns shared weights ×4 MobileNetV3-Small trunk ImageNet init (BSD-3) · gently fine-tuned 8²×48 ×4 four field maps same gated two-expert head reads each turn 32×32 cells voted probability field mean of four logit maps = geometric-mean vote β-sharpened soft-argmax commits to the hottest mode (unchanged) frozen contract (lat, lon, confidence) position fix + confidence confidence 0–1 reads the voted map's shape (unchanged) may abstain invariance by construction, not by data exp 6 taught headings by example — reverted; this builds them in Gaussian-CE + decode L2 + conf BCE — all unchanged the losses now grade the voted answer — train what flies 2.16 · kept · C4 rotation-vote fieldINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY red = this experiment: field-shape confidence + calibrated abstention — unsure crops return no fix 128²×3 camera frame one night exposure frozen contract MobileNetV3-Small trunk ImageNet-pretrained · stem + 8 inverted-residual blocks, unchanged 128²×3 8²×48 layout code 1×1 conv, unchanged 8²×8 48-d GAP unchanged ∑/n raw-pixel mean brightness lighting gate tiny MLP → σ, unchanged g ∈ [0,1] bright head FC 560→1024 dark head FC 48→1024 · GAP-only, unchanged blend (1−g)·b + g·d probability field 32×32 cells β·logits sharpened field softmax(β·logits) β = 3 — unchanged soft-argmax over the sharpened field commits to the dominant peak — unchanged frozen contract (lat, lon, confidence) position fix + confidence Gaussian-CE on the field + L2 on the committed decode both unchanged — localization trains exactly as before peak mass entropy mode−mean gap 0.3 confidence head — learns when to abstain tiny MLP on field shape · calibrated to the frozen 0.3 bar below the bar → no fix, coast on last estimate confidence BCE — did the committed fix land within ~350 m? detached head — cannot disturb localization per-bucket threshold calibration ≥40% keep a fix in every lighting bucket 2.15 · kept · Selective predictionINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY red = this experiment: β-sharpened peak-commit decode replaces mean-of-map averaging 128²×3 camera frame one night exposure frozen contract MobileNetV3-Small trunk ImageNet-pretrained · stem + 8 inverted-residual blocks, unchanged 128²×3 8²×48 layout code 1×1 conv, unchanged 8²×8 48-d GAP unchanged ∑/n raw-pixel mean brightness lighting gate tiny MLP → σ, unchanged g ∈ [0,1] bright head FC 560→1024 dark head FC 48→1024 · GAP-only, unchanged blend (1−g)·b + g·d probability field 32×32 cells β·logits sharpened field — NEW softmax(β·logits), β = 3 peaks boosted, hedging suppressed soft-argmax over the sharpened field commits to the dominant peak frozen contract (lat, lon, confidence) position fix + confidence confidence 0–1 may abstain instead of guessing Gaussian-CE on the unsharpened field (unchanged) the field keeps learning calibrated evidence L2 on the SHARPENED decode the loss now grades the committed answer that flies 2.14 · kept · Peak-commit decodeINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 camera frame one night exposure frozen contract MobileNetV3-Small trunk ImageNet-pretrained · stem + 8 inverted-residual blocks (exp 11, unchanged) 128²×3 8²×48 layout code 1×1 conv, unchanged 8²×8 48-d GAP unchanged ∑/n raw-pixel mean brightness lighting gate tiny MLP → sigmoid g ∈ [0,1] bright head FC 560→1024 dark head — NEW FC 48→1024, GAP-only blend (1−g)b + g·d probability field 32×32 cells, unchanged soft-argmax over ALL cells answer = Σ probability · cell-center frozen contract (lat, lon, confidence) position fix + confidence confidence 0–1 may abstain instead of guessing cross-entropy + decode L2 (unchanged) gate + dark head train on the SAME losses no lighting label — g learns from brightness ⊕ GAP red = this experiment: gated dark-expert head blended with the existing layout head 2.12 · kept · Luminance-gated dark-expert field head blended with the existing layout headINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 camera frame one night exposure frozen contract MobileNetV3-Small encoder — ImageNet-pretrained stem + 8 inverted-residual blocks · depthwise conv + squeeze-excite 64²×16 8²×48 1×1 conv → 8²×8 layout code keeps what sits WHERE in the crop 48-d GAP texture average — as before 512 ⊕ 48 = 560 FC 560 → 1024 logits softmax probability field 32×32 cells over the map soft-argmax over ALL cells answer = Σ probability · cell-center frozen contract (lat, lon, confidence) position fix + confidence confidence 0–1 may abstain instead of guessing trunk weights arrive from ImageNet (1.2M real photos) — not random fine-tuned gently: trunk lr 1e-4 · fresh heads lr 1e-3 · BSD-3 licensed Gaussian-CE + decode L2 + conf BCE — all unchanged head, decode and training recipe identical to kept exp 10 red = this experiment: an ImageNet-pretrained trunk replaces the from-scratch encoder 2.11 · kept · ImageNet-pretrained MobileNetV3-Small trunk replaces the from-scratch encoderINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 camera frame one night exposure frozen contract convolutional encoder Conv 3×3, stride 2 ×4 · BN + ReLU 64²×16 8²×128 1×1 conv → 8²×8 layout code keeps what sits WHERE in the crop 128-d GAP texture average — as before 512 ⊕ 128 = 640 FC 640 → 1024 logits softmax probability field 32×32 cells over the map soft-argmax over ALL cells answer = Σ probability · cell-center frozen contract (lat, lon, confidence) position fix + confidence confidence 0–1 may abstain instead of guessing Gaussian-CE + decode L2 + conf BCE — all unchanged training recipe identical to kept exp 7; only the head's input changed red = this experiment: the field head reads an 8×8 layout code ⊕ texture average 2.10 · kept · Layout-aware field headINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 camera frame one night exposure frozen contract convolutional encoder Conv 3×3, stride 2 ×4 · BN + ReLU 64²×16 8²×128 128-d GAP FC → 1024 logits softmax probability field 32×32 cells over the map soft-argmax over ALL cells answer = Σ probability · cell-center frozen contract (lat, lon, confidence) position fix + confidence confidence 0–1 may abstain instead of guessing = same place training batches become PAIRS — same place, two lighting renders, two headings one shared 6,000-location set across all 6 renders · 32 places × 2 views per step NT-Xent: pull the pair together, push 62 others apart 64-d projection of the descriptor · τ 0.2 · head never flies Gaussian-CE + L2 + conf BCE — unchanged 2.9 · reverted · Cross-lighting contrastive pairsINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 camera frame one night exposure frozen contract + + + residual encoder — 11 conv layers stem + 3 stride-2 stages, each ending in a residual block ~973k params, 4.2× the old 4-conv stack 3.7 of the 4.0 MiB flight-memory budget (was 0.9) 64²×32 8²×160 160-d GAP FC → 1024 logits softmax probability field 32×32 cells over the map soft-argmax over ALL cells answer = Σ probability · cell-center frozen contract (lat, lon, confidence) position fix + confidence confidence 0–1 may abstain instead of guessing Gaussian-CE + L2 + conf BCE (unchanged) same lessons, same 36k crops, same 8 epochs — only the student grew 2.8 · reverted · Deployment-envelope residual encoderCamera frame 1 of 6 lighting renders 128×128×3 64²×16 32²×32 16²×64 8²×128 Feature extractor 4 strided conv blocks · ~230k params 128-d embedding global average pool 128 → 1024 probability field 32×32 map cells · ~220 m/cell soft-argmax expected coordinate over cell centers (lat, lon, confidence) position + confidence u,v ∈ [0,1] map coords → lat, lon conf head (linear + sigmoid) training only was: 800 places/bucket now: 6,000 places/bucket of ~45,000 available ×7.5 coverage — every map cell now trained in every lighting bucket; ~4,500 steps (was 600) Gaussian-CE on smoothed cell target + coord L2 + conf BCE (unchanged) one static crop per place at one random heading (unchanged, per exp-6) 2.7 · kept · Scale training coverage 7.5xINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 camera frame one night exposure frozen contract convolutional encoder Conv 3×3, stride 2 ×4 · BN + ReLU 64²×16 8²×128 128-d GAP FC → 1024 logits softmax probability field 32×32 cells over the map soft-argmax over ALL cells answer = Σ probability · cell-center frozen contract (lat, lon, confidence) position fix + confidence confidence 0–1 may abstain instead of guessing crop rotations re-drawn EVERY epoch fresh views of the same places each pass, not one frozen tensor reused 8× Gaussian-CE + L2 (unchanged) 2.6 · reverted · Per-epoch rotation resampling replaces the one-shot frozen training tensorINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 camera frame one night exposure frozen contract convolutional encoder Conv 3×3, stride 2 ×4 · BN + ReLU 64²×16 8²×128 128-d GAP FC → 1024 logits softmax probability field 32×32 cells over the map soft-argmax over ALL cells answer = Σ probability · cell-center frozen contract (lat, lon, confidence) position fix + confidence confidence 0–1 may abstain instead of guessing sum-pool the SAME field to 16², 8², 4² — Gaussian-CE at every scale coarse cells collect 10–40 positives each and steer the fine field (+ L2 on decode, unchanged) 2.5 · reverted · Hierarchical coarse-to-fine supervision of the probability field via probability poolingINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 camera frame one night exposure frozen contract convolutional encoder Conv 3×3, stride 2 ×4 · BN + ReLU 64²×16 8²×128 dilated conv 3×3, d2 ~95 px context per cell 64 per-patch coordinates 1×1 conv + σ (8×8×2) mean of 64 answers one committee answer from 64 votes frozen contract (lat, lon, confidence) position fix + confidence confidence 0–1 may abstain instead of guessing smooth-L1: each patch vs its OWN true coordinate crop center + rotated offset → 64× denser supervision 2.4 · reverted · ACE-style dense per-patch scene-coordinate regression replaces global map-cell probability fieldINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 camera frame one night exposure frozen contract convolutional encoder Conv 3×3, stride 2 ×4 · BN + ReLU 64²×16 8²×128 128-d GAP FC → 1024 logits softmax probability field 32×32 cells over the map argmax → 5×5 window soft-argmax commit to the hottest cell, refine locally frozen contract (lat, lon, confidence) position fix + confidence confidence 0–1 may abstain instead of guessing cross-entropy vs Gaussian cell target (unchanged) L2 on the WINDOWED decode train on exactly what flies 2.3 · reverted · Argmax-anchored local soft-argmax decode replaces global expected-coordinateINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 camera frame one night exposure frozen contract convolutional encoder Conv 3×3, stride 2 ×4 · BN + ReLU 64²×16 8²×128 128-d GAP FC → 1024 logits softmax probability field 32×32 cells over the map soft-argmax over ALL cells answer = Σ probability · cell-center frozen contract (lat, lon, confidence) position fix + confidence confidence 0–1 may abstain instead of guessing cross-entropy: match a Gaussian bump on the true cell σ = 1.5 cells — every cell gets a hotter/colder gradient L2 on the decoded (u, v) 2.2 · kept · DSNT-style spatial probability field over the map replaces direct (u,v) regressionINFERENCE PATH — WHAT FLIES TRAINING SIGNALS — NEVER FLY 128²×3 camera frame one night exposure frozen contract convolutional encoder Conv 3×3, stride 2 ×4 · BN + ReLU 64²×16 8²×128 128-d vector global avg pool FC 128 → 3 sigmoid one shot frozen contract (lat, lon, confidence) position fix + confidence mean-squared error on (u, v) the whole map supervises one number pair BCE ×0.1 on confidence starting design — nothing changed yet 2.1 · kept · Starting baseline — naive TinyLocNet, from-scratch, frozen pipeline v1Our training data
Everything the model knows, it learned from one picture: an open-licensed
aerial orthophoto mosaic of Berlin from the Berlin/Brandenburg geoportal
(© GeoBasis-DE/LGB, dl‑de/by‑2‑0, modified),
fetched by the frozen pipeline and resampled to 1 m per pixel —
6,939 × 6,828 pixels covering the whole bounding box. No
Google or Bing tiles anywhere: only open geodata, so the entire project can
be published. Training crops 128 m frames from every position on that
mosaic; the held-out eval frames stand on the same ground, framed from
positions and headings training never used. A hundred of the 2,970
tiles:
52.52829 · 13.32061 52.52845 · 13.33192 52.52858 · 13.34135 52.52874 · 13.35267 52.52890 · 13.36398 52.52903 · 13.37342 52.52918 · 13.38473 52.52934 · 13.39605 52.52946 · 13.40548 52.52962 · 13.41680 52.52139 · 13.32087 52.52155 · 13.33218 52.52168 · 13.34161 52.52184 · 13.35293 52.52200 · 13.36424 52.52213 · 13.37367 52.52228 · 13.38498 52.52243 · 13.39630 52.52256 · 13.40573 52.52271 · 13.41704 52.51564 · 13.32109 52.51580 · 13.33240 52.51593 · 13.34183 52.51609 · 13.35314 52.51624 · 13.36445 52.51637 · 13.37388 52.51653 · 13.38520 52.51668 · 13.39651 52.51681 · 13.40594 52.51696 · 13.41725 52.50874 · 13.32135 52.50890 · 13.33266 52.50903 · 13.34209 52.50919 · 13.35340 52.50934 · 13.36471 52.50947 · 13.37414 52.50963 · 13.38545 52.50978 · 13.39676 52.50991 · 13.40619 52.51006 · 13.41750 52.50298 · 13.32157 52.50314 · 13.33288 52.50328 · 13.34231 52.50343 · 13.35361 52.50359 · 13.36492 52.50372 · 13.37435 52.50388 · 13.38566 52.50403 · 13.39697 52.50416 · 13.40639 52.50431 · 13.41771 52.49608 · 13.32183 52.49624 · 13.33314 52.49637 · 13.34256 52.49653 · 13.35387 52.49669 · 13.36518 52.49682 · 13.37460 52.49697 · 13.38591 52.49713 · 13.39722 52.49726 · 13.40664 52.49741 · 13.41795 52.49033 · 13.32205 52.49049 · 13.33336 52.49062 · 13.34278 52.49078 · 13.35409 52.49094 · 13.36539 52.49107 · 13.37482 52.49122 · 13.38612 52.49138 · 13.39743 52.49150 · 13.40685 52.49166 · 13.41816 52.48343 · 13.32232 52.48359 · 13.33362 52.48372 · 13.34304 52.48388 · 13.35435 52.48404 · 13.36565 52.48417 · 13.37507 52.48432 · 13.38638 52.48447 · 13.39768 52.48460 · 13.40710 52.48475 · 13.41841 52.47768 · 13.32253 52.47784 · 13.33384 52.47797 · 13.34326 52.47813 · 13.35456 52.47829 · 13.36586 52.47841 · 13.37528 52.47857 · 13.38659 52.47872 · 13.39789 52.47885 · 13.40731 52.47900 · 13.41861 52.47078 · 13.32280 52.47094 · 13.33410 52.47107 · 13.34352 52.47123 · 13.35482 52.47138 · 13.36612 52.47151 · 13.37554 52.47167 · 13.38684 52.47182 · 13.39814 52.47195 · 13.40756 52.47210 · 13.41886 4
How to use it One file, one function, three steps.
1. Download the model: berlin.onnx (3.1 MB). It runs anywhere ONNX
runs — a laptop, a Raspberry Pi, the ESP32‑P4 target.
2. Feed it camera frames, 3. trust it only when it is sure —
the whole client is this:
# pip install onnxruntime opencv-python pyproj numpy
import cv2, numpy as np, onnxruntime as ort
from pyproj import Transformer
# the Berlin model's georeference (from the training raster's metadata)
ORIGIN = (385883.0, 5821316.0) # UTM 33N of the map's top-left corner
SIZE = (6939, 6828) # map size in px; 1 px = 1 m
to_wgs84 = Transformer.from_crs("EPSG:32633", "EPSG:4326")
sess = ort.InferenceSession("berlin.onnx")
cam = cv2.VideoCapture(0) # the downward-facing camera
ok, bgr = cam.read() # 1 - grab one frame
rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)
rgb = cv2.resize(rgb, (128, 128)) # frame should cover ~128 m of ground
x = rgb.astype(np.float32).transpose(2, 0, 1)[None] / 255.0
u, v, conf = sess.run(None, {"frame": x})[0][0] # 2 - ask the network
lat, lon = to_wgs84.transform(ORIGIN[0] + u * SIZE[0],
ORIGIN[1] - v * SIZE[1])
if conf < 0.3: # 3 - trust it only when it is sure
print("no fix - the model abstains")
else:
print(f"{lat:.5f}, {lon:.5f} (confidence {conf:.2f})")
The frame must look straight down over Berlin and
cover roughly 128 m of ground (about 100 m altitude with a
typical lens). Below confidence 0.3 the fix is discarded, exactly as the
research scores it.
5
Explore the record Every experiment, every design, every dead end.
6
Proven alternatives GPS-denied localisation is not unsolved — this just walks a different road.
GPS-denied visual localization is not an unsolved problem. The
established, field-tested family matches live camera frames against
georeferenced reference imagery carried on the aircraft — e.g.
WildNav
(Vision-based GNSS-Free
Localization for UAVs in the Wild ), which matches UAV photographs
against satellite tiles with deep feature matching and demonstrated
GNSS-comparable accuracy in real flights. If you need working GPS-denied
navigation today, start there, not here.
This project deliberately walks a different road, for two reasons.
Licensing: the strongest matchers in that stack — Magic Leap's
SuperGlue
/ SuperPoint
pretrained networks — are licensed for noncommercial research only, without
the right to sublicense, which is incompatible with a fully open-sourceable,
commercially usable system (permissive alternatives like
LightGlue (Apache-2.0)
exist, but the whole approach still means shipping reference imagery on
the airframe — this project's hardest constraint rules that out).
Curiosity: the actual motivation is ground-level research — can an
autonomous loop of coding agents discover a genuinely different approach,
with no reference imagery on board and the map living entirely in the
network's weights, under a low-light premise? It may or may not end up
competitive with the field-tested systems above. Saying so out loud is
part of the experiment.
The vision, if it works: a novel algorithm that lets anyone draw
a bounding box around a place they care about and generate their own tiny,
punchy, self-contained geo-boxed model — reliable, personalized, and free.
Open data in, open weights out; no reference imagery to license, no vendor
to call, no cloud to depend on. The map is yours, and it lives in a few
megabytes you own.
updated 2026-08-01 14:57 UTC · experiments run on a single local
machine; the loop commits every result to git as it goes
