| --- |
| license: cc-by-4.0 |
| datasets: |
| - risenyard/egms-qa-dataset |
| tags: |
| - safetensors |
| - remote-sensing |
| - insar |
| - ground-motion |
| - time-series |
| - egms |
| --- |
| |
| # EGMS-QA Encoder |
|
|
| Pretrained encoder for EGMS displacement time series within 7 km tiles. It |
| produces 256-dimensional point representations, which are spatially pooled |
| into 65 tile tokens. |
|
|
| This repository contains the released weights, model settings, training recipe, |
| and evaluation results. |
|
|
| ## Use the model |
|
|
| See the [architecture on GitHub](https://github.com/risenyard/egms-qa/blob/main/src/egms_encoder/README.md#architecture), |
| then follow [installation and a one-tile run](https://github.com/risenyard/egms-qa/blob/main/src/egms_encoder/README.md#installation) to try the encoder. |
| The same guide covers [local inputs](https://github.com/risenyard/egms-qa/blob/main/src/egms_encoder/README.md#use-local-inputs) |
| and [training, resuming, and token extraction](https://github.com/risenyard/egms-qa/blob/main/src/egms_encoder/README.md#reproduce-training). |
| Prepared tiles and precomputed tokens are available in the |
| [Dataset repository](https://huggingface.co/datasets/risenyard/egms-qa-dataset#encoder-data). |
|
|
| ## Files |
|
|
| | file | purpose | |
| |---|---| |
| | [encoder.safetensors](encoder.safetensors) | encoder weights | |
| | [config.json](config.json) | model architecture and input dimensions | |
| | [normalization.json](normalization.json) | input mean, standard deviation, and residual scale | |
| | [training_args.json](training_args.json) | training recipe and checkpoint-selection record | |
| | [eval_results.json](eval_results.json) | masked-reconstruction metrics | |
|
|
| Inference requires the weights, model config, and normalization. The Dataset |
| repository provides the measurements and split manifest. |
|
|
| ## Input requirements |
|
|
| | input or output | contract | |
| |---|---| |
| | tile displacement | vertical displacement in mm, `[N,294]` | |
| | coordinates | EPSG:3035 easting and northing in meters, `[N,2]` | |
| | model preprocessing | checkpoint normalization and centered coordinates | |
| | point representations | `[N,256]` | |
| | pooled tokens | `[65,256]` and a 65-element validity mask | |
|
|
| The Dataset stores `[0,294)`, corresponding to `[8,302)` on the 304-step |
| source-preparation axis. Its data config retains the source offset and six-day |
| cadence for physical-time calculations. |
|
|
| Use this checkpoint's normalization for inference. When training a new encoder |
| on another corpus, fit normalization on that corpus's training split. |
|
|
| ## Evaluation |
|
|
| Evaluation covers 1,000 held-out tiles with 2,047,451 point histories. A central |
| 88-step interval, approximately 30% of the 294-step input, is masked at the |
| same positions for every point in a tile. |
|
|
| | metric | value | |
| |---|---:| |
| | normalized MSE | 0.0702 | |
| | MSE | 2.279 mm² | |
| | RMSE | 1.510 mm | |
| | MAE | 1.007 mm | |
| | pooled EGMS residual | 1.433 mm | |
| | per-point RMSE P10 / P50 / P90 | 0.54 / 1.03 / 2.43 mm | |
|
|
| These values describe reconstruction of held-out observations under the |
| specified masking protocol. |
|
|
| ## Scope and license |
|
|
| The encoder consumes prepared EGMS-QA tiles. Official-product downloading, |
| format conversion, and preparation of another reference period require a |
| separate workflow. Its outputs describe observed deformation and do not |
| establish causes, predict future motion, or certify structural safety. |
|
|
| The encoder is released under CC-BY-4.0. |
|
|
