arXiv AI June 16, 2026 By Muhammad Taha Mukhtar, Syed Musa Ali Kazmi, Khola Naseem, Muhammad Ali Chattha, Andreas Dengel, Sheraz Ahmed, Muhammad Naseer Bajwa, Muhammad Imran Malik

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

Read the original on arXiv AI →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 30

Automated Quality Assessment of Geospatial Vector Data: A GeoAI Approach using Spatial Representation Learning

arXiv:2606. 28390v1 Announce Type: cross Abstract: Geospatial vector data quality is a foundational research topic in GIS, yet classic rule-based quality assessment algorithms often struggle with diverse urban morphologies and massive data volumes.

By Hao Li, Chen Chu, Filip Biljecki, Cyrus Shahabi, Wenwen Li

agents diffusion benchmarks data-engineering

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

Jun 16

Bridging Geographic Bias in Urban Streetscape Inference via Lifelong Learning with Visual-Semantic Pivoting

arXiv:2606. 15055v1 Announce Type: cross Abstract: Visual perception of urban streetscapes underpins evidence-based decisions in landscape planning, public health, and place-making.

By Xinze Zhang

benchmarks safety

arXiv AI

Jun 12

PlaceRep: Geospatial Place Representation Learning from Large-Scale Point-of-Interest Data

arXiv:2507. 02921v4 Announce Type: replace-cross Abstract: Learning effective representations of urban environments requires capturing spatial structure beyond fixed administrative boundaries.

By Mohammad Hashemi, Hossein Amiri, Andreas Zufle

rag benchmarks

arXiv AI

Jun 2

CAFOSat: A Strongly Annotated Dataset for Infrastructure-Aware CAFO Mapping Using High-Resolution Imagery

arXiv:2606. 00548v1 Announce Type: cross Abstract: Concentrated Animal Feeding Operations (CAFOs) play an important role in agricultural production but are also associated with environmental, public health, and disease surveillance concerns.

By Oishee Bintey Hoque, Nibir Chandra Mandal, Mandy L Wilson, Samarth Swarup, Madhav Marathe, Abhijin Adiga

llms 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

Related stories

Automated Quality Assessment of Geospatial Vector Data: A GeoAI Approach using Spatial Representation Learning

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

Bridging Geographic Bias in Urban Streetscape Inference via Lifelong Learning with Visual-Semantic Pivoting

PlaceRep: Geospatial Place Representation Learning from Large-Scale Point-of-Interest Data

CAFOSat: A Strongly Annotated Dataset for Infrastructure-Aware CAFO Mapping Using High-Resolution Imagery

OSMGraphCLIP: Learning Global Location Representations from OpenStreetMap Graphs