arXiv Machine Learning

DeepC4: Deep Conditional Census-Constrained Clustering for Large-scale Multitask Spatial Disaggregation of Urban Morphology

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.

arXiv Machine Learning

Jul 20

DELUGE: Towards Continental-Scale Daily Pluvial Flood Damage Prediction via Interpretable Conditioning on Foundation Model Embeddings

arXiv:2607. 16050v1 Announce Type: new Abstract: Pluvial (rainfall-driven) flooding accounts for 45% of National Flood Insurance Program (NFIP) claims in the United States and is harder to predict than its riverine and coastal counterparts, with existing approaches limited to coarse resolution, regional domains, or computationally intensive process-based models unsuitable for daily continental-scale use.

By Yuya Kawakami, Daniel Cayan, Dongyu Liu, Kwan-Liu Ma, Tom Corringham

rag multimodal safety

arXiv AI

Jun 16

SLUM-i: Semi-supervised Learning for Urban Mapping of Informal Settlements and Data Quality Benchmarking

arXiv:2602. 04525v2 Announce Type: replace-cross Abstract: Rapid urban expansion has fueled the growth of informal settlements in major cities of low- and middle-income countries, with Lahore and Karachi in Pakistan and Mumbai in India serving as prominent examples.

By Muhammad Taha Mukhtar, Syed Musa Ali Kazmi, Khola Naseem, Muhammad Ali Chattha, Andreas Dengel, Sheraz Ahmed, Muhammad Naseer Bajwa, Muhammad Imran Malik

computer-vision benchmarks data-engineering

arXiv Machine Learning

Jun 19

Exploring the potential of AlphaEarth and TESSERA embeddings for Fine-scale Local Climate Zone Mapping: A case study across five cities in Switzerland

arXiv:2606. 20034v1 Announce Type: new Abstract: Understanding urban spatial morphology is critical for climate modeling, risk assessment, and sustainable urban design, and Local Climate Zone (LCZ) mapping provides the basic framework for this.

By Htet Yamin Ko Ko, Clement Atzberger

rag data-engineering

Hugging Face Trending Papers

Sep 14

Transfer Learning for Socioeconomic Estimation in Forced-Displacement Settings

Progress in inclusive household surveys has strengthened socioeconomic evidence for forcibly displaced populations, providing indispensable benchmarks on living conditions and welfare. However, these...

llms computer-vision fine-tuning multimodal benchmarks

arXiv AI

Jul 24

Joint Utilization of Geospatial and census proxies for Autoencoder-Assisted Downscaling (JUGAAD) of socioeconomic indicators in India

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 AI

Sep 15

Transfer Learning for Socioeconomic Estimation in Forced-Displacement Settings

The paper presents a transfer‑learning approach that adapts a multimodal spatiotemporal vision transformer, originally trained on Demographic and Health Survey data, to estimate socioeconomic conditions in forced‑displacement settings. Using satellite‑derived geospatial covariates, the adapted model explains up to 66% of variation in socioeconomic outcomes in camp‑intersecting grids and 41% in non‑camp areas, achieving mean absolute errors of 4.37 and 5.41 index points respectively. This framework supplements periodic household surveys by providing regularly updated, spatially granular socioeconomic estimates that bridge data gaps between survey rounds.

By Steven Ndung'u, Adel Daoud, Ismael Yacoubou Djima, Hai-Anh H. Dang, Patrick Michael Brock

llms computer-vision fine-tuning multimodal benchmarks

arXiv AI

Jun 9

OSMGraphCLIP: Learning Global Location Representations from OpenStreetMap Graphs

arXiv:2606. 08046v1 Announce Type: new Abstract: We present OSMGraphCLIP, a CLIP-style geospatial representation model that learns global location embeddings from freely available OpenStreetMap (OSM) data.

By Dimitrios Michail, Eleni Saka, Ioannis Giannopoulos, Ioannis Papoutsis

rag benchmarks safety

arXiv AI

Jul 29

DisasterTD: Disaster Toponym Disambiguation Using Multimodal LLMs and Cross-View Geolocalization

arXiv:2607. 24856v1 Announce Type: cross Abstract: Social media imagery (SMI) provides timely and fine-grained ground perspectives that are valuable for situational awareness and emergency response.

By Wenping Yin, Ziqi Liu, Naixia Mou, Weijia Li, Danfeng Hong, Hao Li

llms multimodal benchmarks

arXiv Computer Vision

Sep 1

Multi-Sensor Mapping of Vulnerable Urban Settlements Using SAR, Multispectral, and Hyperspectral Imagery: A Case Study in C\'ordoba, Argentina

The paper introduces a multi‑sensor deep learning framework for mapping informal settlements in Córdoba, Argentina, using high‑resolution PlanetScope multispectral imagery, COSMO‑SkyMed SAR data, and medium‑resolution PRISMA hyperspectral observations. It compares SAR‑only, MS‑only, and various fusion strategies (early, middle, late) and finds that late fusion with hyperspectral data (LF+HS) delivers the best balance of classification accuracy and spatial precision. The study also demonstrates that detections outside official polygons align with broader municipal vulnerability layers and that identified settlements show higher surface temperatures during a heatwave, highlighting localized heat amplification.

By Luigi Russo, Anabella Ferral, Silvia Liberata Ullo, Paolo Gamba

arXiv Machine Learning

Aug 24

Keep Your Friends Close, and the Right Neighbours Closer: Disaster-Conditioned Kernel-Regularized Graph Attention for Building Damage Classification

arXiv:2608.20548v1 Announce Type: cross Abstract: Disaster damage is spatial: buildings rarely fail in isolation. Yet using spatial context for damage classification remains surprisingly underexplore...

By Fuad Hasan, Chul Min Yeum

arXiv Machine Learning

Aug 27

Beyond Point Predictions: Uncertainty-Aware Satellite Poverty Mapping for Public Policy

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

llms

arXiv Machine Learning

Aug 19

A multi-view contrastive learning framework for spatial embeddings in risk modelling

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

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