Puffin is a probabilistic framework that disaggregates coarse spatial totals into high‑resolution estimates by leveraging satellite embeddings as covariates. Unlike deterministic methods, it learns a probability distribution for each fine‑scale subregion and trains with an aggregation‑aware likelihood, ensuring that predictions sum to the observed regional total. The approach provides calibrated uncertainty and does not require fine‑resolution labels, and it has been evaluated on German and US census, employment, and election data.
DeepC4 is a deep learning-based spatial disaggregation method that uses local census statistics as cluster-level constraints and incorporates multiple conditional label relationships in a multitask learning framework. Applied to Rwandan urban morphology, it achieves macro‑F1 scores of 0.63, 0.78, and 0.45 for roof, wall, and height prediction, respectively, and estimates national dwelling and occupant counts within about 1.1% error compared to census records. The approach outperforms existing GEM and METEOR methods and covers 32‑49% more 500‑meter grid pixels across provinces.
By Joshua Dimasaka, Christian Gei{\ss}, Emily So
arXiv:2608. 08064v1 Announce Type: new Abstract: Learning fine-grained spatial patterns from coarse-resolution data is challenging, especially in causal settings where high-resolution effects must be inferred from aggregated interventions and outcomes.
By Gerrit Gro{\ss}mann, Sumantrak Mukherjee, Sebastian J. Vollmer
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
The paper presents an uncertainty‑aware machine‑learning approach for mapping poverty in Africa using satellite imagery. By combining simultaneous quantile regression with a novel conformal prediction technique, the authors generate statistically guaranteed prediction intervals for neighborhood‑level International Wealth Index estimates, achieving high explanatory power (R² = 0.75) while acknowledging broader uncertainty. They also propose a risk‑controlled aid allocation procedure that leverages both survey data and model predictions, showing in simulations that it can deliver more aid per eligible recipient than alternative strategies.
By Markus B. Pettersson, James Bailie, Mohammad Kakooei, Eagon Meng, Adel Daoud
The paper introduces a multi‑view contrastive learning framework that creates low‑dimensional spatial embeddings by combining satellite imagery and OpenStreetMap data across Europe. These embeddings align with coordinate‑based encodings, allowing any dataset with latitude‑longitude pairs to be enriched with meaningful spatial features without needing the original spatial inputs. In case studies on French real‑estate prices and Belgian flood claim counts, models using the embeddings outperform those using raw coordinates, improving predictive accuracy and territorial risk classification while offering explainable spatial effects.
By Freek Holvoet, Christopher Blier-Wong, Katrien Antonio