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 using an aggregation-aware likelihood, ensuring that predictions sum to the observed regional totals. 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.
By Chaitanya Jobanputra, Sebastian Vollmer, Gerrit Gro{\ss}mann
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 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
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 introduces DSP‑BART‑HS, a Dynamic Spatial Panel Bayesian Additive Regression Trees model with Horseshoe shrinkage, designed for high‑dimensional spatio‑temporal panel data. Across nine simulated scenarios, the model outperforms or matches a wide range of spatial econometric, non‑parametric machine learning, and small‑area estimators, especially when individual‑level non‑linearity drives outcome variance. The authors validate the method on two U.S. county‑level applications—intergenerational economic mobility and geographic income inequality—showing strong predictive accuracy even under unseen‑region, random, and temporal holdouts, while noting a temporal extrapolation advantage for a simpler autoregressive model.
By Hammed A. Olayinka, Saheed O. Olayemi
arXiv:2607. 20559v1 Announce Type: cross Abstract: Monitoring poverty and food security indicators is imperative for addressing socioeconomic challenges in developing nations.
By Aditya Dutt, Paul Gader, Aditya Singh
arXiv:2607. 10410v1 Announce Type: cross Abstract: Reliable forecasting of several interrelated environmental variables - such as regional precipitation and temperature, or other correlated geophysical fields - across many locations calls for accurate predictions accompanied by trustworthy statements of their uncertainty.
By Jongwook Kim, Jong-Min Kim
MoRA is a human‑centric geospatial representation learning framework that uses a large mobility graph as its backbone to fuse spatial tokenization, graph neural networks, and asymmetric contrastive learning. It aligns over 100 million points of interest, massive remote sensing imagery, and structured demographic data with a billion‑edge mobility graph, producing compact 128‑dimensional embeddings that capture socio‑economic context and functional roles of locations. On a benchmark of nine downstream social and economic prediction tasks, MoRA outperforms state‑of‑the‑art models by an average of 12.9% and demonstrates scaling behavior analogous to large language models.
By Ya Wen, Jixuan Cai, Qiyao Ma, Linyan Li, Xinhua Chen, Chris Webster, Yulun Zhou
arXiv:2609.15305v1 Announce Type: cross
Abstract: Urban region representation learning commonly combines heterogeneous data sources, such as mobility flows, points of interest, and land-use informati...
By Sean Bin Yang, Ying Sun, Zongyi Xu, Tung Kieu, Jilin Hu, Bin Yang, Kristian Torp, Hua Lu, Torben Bach Pedersen
arXiv:2607. 13558v1 Announce Type: new Abstract: Urban region profiling constitutes a core problem in urban computing, supporting applications such as population estimation, economic assessment, and environmental monitoring.
By Xixuan Hao, Yutian Jiang, Jiabo Liu, Yihang Yang, Guangyin Jin, Song Gao, Yuxuan Liang