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arXiv AI

PlaceSeek: Human-Centered Geospatial Retrieval of Urban Outdoor Places via Semantic Grounding and Affective Alignment

PlaceSeek is a human‑centered geospatial retrieval framework that maps natural‑language queries to street‑view images by decomposing queries into functional and affective sub‑intents. It uses a Semantic Grounding Module to verify that candidate images contain the physical evidence needed for the intended activity, and an Affective Alignment Module to re‑rank these candidates based on human urban perception judgments. Evaluated on 31,956 Milan street‑view locations, PlaceSeek achieves high precision and ranking metrics, outperforming several vision‑language baselines and demonstrating the importance of both physical grounding and affective alignment for complex urban spatial queries.

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arXiv Machine Learning
Sep 1

BEACON: Behavioral and Semantic Enrichment of AlphaEarth Embeddings through Tri-Modal Contrastive Learning

BEACON is a tri‑modal contrastive learning framework that enriches AlphaEarth embeddings by aligning physical representations from Earth‑observation imagery with semantic POI text and human behavioral POI visitation data, while keeping the deployed model image‑only. In a Houston case study, BEACON outperformed six baselines on nine downstream tasks, achieving up to 43% higher R² for obesity prevalence, 34% for poor mental health, and 22% for median household income under a linear probe.

By Hao Tian, Heng Cai, Yifan Yang
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arXiv AI
4d ago

A Decade of PhotoMappers: A Longitudinal Study of Crowdsourced Disaster Photos Geolocalization with Vision-Language Models and Geospatial Reasoning

arXiv:2610.15403v1 Announce Type: new Abstract: Crowdsourced disaster imagery provides timely, ground-level observations when conventional disaster mapping is delayed or incomplete, yet its geographi...

By Hao Li, Wenping Yin, Fabian Deuser, Jia Jia, Ziqi Liu, Levente Juh\'asz, Fan Zhang, Filip Biljecki
llmsmultimodal
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arXiv Machine Learning
Oct 2

Network-based Spatial Context Retrieval for Open-weight LLMs: A Faithfulness Benchmark for Grounded Geographic Reasoning

The paper introduces a network‑based spatial context retrieval pipeline that uses pedestrian street networks and open data (OpenStreetMap, GHS‑POP) to generate compact spatial briefs for open‑weight large language models. It then builds a faithfulness benchmark that labels each model claim by its source—whether grounded in the brief or drawn from training knowledge—and tests models against planted false premises across multiple cities and model configurations. The study finds that model family and generation influence resistance to false premises more than model size, revealing dimensions of spatial reasoning not captured by traditional correctness metrics.

By Joan Perez
llmsbenchmarks
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arXiv AI
Aug 25

GeoRisk-RAG: A Hierarchy-Aware Risk Framework for Improving RAG Reliability through Selective Answering

GeoRisk-RAG is a hierarchy‑aware framework that improves the reliability of Retrieval‑Augmented Generation (RAG) by incorporating geographic validity. It estimates geographic applicability using a Directed Acyclic Graph (DAG)‑based distance during context retrieval, enabling selective answering. Experiments on a wildfire‑related QA dataset show that GeoRisk‑RAG reduces false confidence rates from ~0.090 to 0.009 and aligns better with human preferences.

By Meenu Ravi, Shailik Sarkar, Lulwah AlKulaib, Yordanos Tessema, Chang-Tien Lu
llmsragreinforcement-learningsafety
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arXiv AI
Oct 12

Mind the Spatial Gap: A Benchmark for Geospatial Reasoning and Constrained Spatial Editing

The paper introduces GeoSpatialBench, a benchmark for evaluating spatial reasoning and constrained spatial editing in multimodal large language models. It tests 28 spatial relations across GeoJSON and map image data from six cities, revealing that models perform better on GeoJSON than images and struggle with quantitative metric relations and editing tasks that must satisfy spatial constraints. The study highlights a significant gap between recognizing spatial relations and actively manipulating them in AI systems.

By Zheng Ren, Yanning Wang, Ziheng Huang, Fei Huang, Shiqi Wang, Ziqian Ye, Yang Yue
llmsroboticsmultimodalbenchmarks
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arXiv Machine Learning
Sep 22

MMS-VPR: A Fine-Grained Multimodal Street-Level Visual Place Recognition Dataset and Evaluation Benchmark for Dense Pedestrian Environments

arXiv:2505.12254v3 Announce Type: replace-cross Abstract: Existing visual place recognition (VPR) datasets predominantly rely on vehicle-mounted imagery, offer limited multimodal diversity, and under...

By Yiwei Ou, Xiaobin Ren, Ronggui Sun, Guansong Gao, Kaiqi Zhao, Manfredo Manfredini
llmsmultimodalbenchmarks
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arXiv AI
Oct 2

Decoding the Disaster: Multi-Task Geospatial Reasoning with Vision-Language Models and Crowdsourced Imagery for Disaster Mapping

The paper introduces GRDisaster, a multi-task geospatial reasoning framework that leverages vision‑language models to interpret, geolocalize, and assess damage in crowdsourced disaster imagery. It builds on a new benchmark dataset of 26,340 images from PhotoMappers, linking volunteer geographic information, street‑view imagery, and remote sensing data across multiple disaster events from 2018 to 2024. GRDisaster combines deterministic and probabilistic cross‑view geolocalization with multi‑view fusion, and introduces spatial reasoning indicators to validate cross‑view matches and quantify disaster severity using expert‑verified annotations.

By Wenping Yin, Fabian Desuer, Ziqi Liu, Naixia Mou, Weijia Li, Pedram Ghamisi, Xiao Xiang Zhu, Hao Li
llmsmultimodalbenchmarkssafety
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