arXiv Machine Learning
5d ago

Do Location Encoders Capture Spatial Effects? A GeoShapley Benchmark Across Scales

Location encoders transform geographic coordinates into high‑dimensional embeddings for machine learning, yet it is unclear how well these embeddings capture interpretable spatial effects. This study benchmarks GeoShapley—a game‑theoretic explainer treating all location features as a single joint player—against eleven TorchSpatial encoders on a synthetic process with known coefficients, across grid, county, and global scales, with and without raw coordinates and under different training regimes. The results show that primary coefficient recovery is consistently high across encoders, while secondary coefficient recovery varies more with scale, especially at the global level, and raw‑coordinate baselines remain competitive throughout.

By Daniel Kiv, Shaowen Wang
arXiv Machine Learning
Sep 23

MIND the Gap: A Geographic Implicit Neural Representation with Adjustable Spatial Scale

The paper introduces MIND, a method that distills specialist geospatial model embeddings into a single generalist coordinate embedding with adjustable spatial granularity, using nested supervision across multiple embedding dimensions. MIND’s design allows downstream predictors to use only leading chunks or apply a Chunked Penalty to downweight finer details without retraining the INR. The authors evaluate MIND on CoordBench, a large INR benchmark of 52 datasets and 78 targets, and report that MIND and its Chunked Penalty variant achieve the highest regression and classification scores, especially under regional holdout, establishing a new state‑of‑the‑art for geographic implicit neural representations.

By Isaac Corley, Arjun Rao, Esther Rolf, Konstantin Klemmer, Evan Shelhamer, Nils Lehmann, Marc Ru{\ss}wurm, Gengchen Mai, Nathan Jacobs, Hannah Kerner
arXiv Machine Learning
Sep 25

Efficient Continuous DEM Reconstruction under Limited Target-Resolution Supervision

The paper introduces SCOPE, a method for reconstructing high‑resolution digital elevation models (DEMs) from coarser‑resolution training pairs. SCOPE learns a continuous terrain representation by predicting a latent coefficient field on a low‑resolution grid and reusing local Fourier residual functions, thereby decoupling coefficient prediction from output‑grid construction. Experiments on diverse land–ocean datasets show that SCOPE outperforms competing methods across six metrics, reduces reconstruction error by about 12 % at three‑times‑unseen scale, and achieves these gains with only a modest increase in computational cost.

By Zekai Shi, Meng Zhang, Haokun Zhang, Bo Zhang