Geographic Information Systems Studies

Últimos artículos

• GeoGAT: Bidirectional Temporal Sampling Meets Hierarchical Graph Attention for Global Video Geo-localization

  Junchao Cui, Xuanzi Ma, Wenqi Shi, Hangyu Li, Biru Zhu, Chong Fu, Xiangyang Luo · 1 de octubre de 2026

  Global video geo-localization aims to infer the geographic location of a video worldwide, evaluating performance across four geographic hierarchies: city, state/province, country, and continent. Existing methods typically employ one-way uniform sampling to process video frames and train independent …

• GeoOutageBench: Benchmarking Ambiguity-aware, Ontology-grounded Geospatiotemporal KGQA for Multimodal Power Outage and Resilience Analysis

  Ethan D. Frakes, Amy Kvien, Rishabh Kundu, Redad Mehdi, Van D. Tran, Vibha S. Mandayam, Kristopher O. Davis, Erika I. Barcelos, Roger H. French, Yinghui Wu, Mengjie Li · 30 de septiembre de 2026

  We introduce GeoOutageBench, a benchmark for assessing LLM-based geospatiotemporal KGQA for multimodal outage and resilience analysis. Unlike existing KGQA benchmarks for Web knowledge, GeoOutageBench considers a spatiotemporal KG that integrates visual, textual, and structured data from outage reco…

• Gradient-Guided Decoupled Adaptation for Geospatial Vision-Language Models

  Dongdong Wang, Deepak Balakrishnan, Ravi Srinivasan, Shenhao Wang · 29 de septiembre de 2026

  Existing geospatial vision-language models (Geo-VLMs) typically optimize diverse geospatial tasks through a unified multi-task adaptation paradigm without explicitly accounting for the heterogeneous optimization characteristics. Our empirical observations reveal heterogeneous gradient characteristic…

• SatNav: A Scalable Benchmark for Long-Horizon UAV Vision-Language Navigation from Satellite Imagery

  Jiajun Jiang, Chunliang Hua, Zichun Chen, Yanxing Wu, Zeyuan Yang, Jie Song, Xiao Hu · 28 de septiembre de 2026

  Urban uncrewed aerial vehicle (UAV) vision-language navigation (VLN) requires agents to follow instructions across extended urban spaces, inherently demanding long-term memory and geospatial grounding. However, scaling existing benchmarks remains difficult because of their reliance on costly reconst…

• Recoverable Geographic Location Information in Earth-Observation Embeddings

  Peiwen Zhang, Kristie Hu, Jovana Knezevic, Shunde Yin, Kyle Gao · 25 de septiembre de 2026

  Earth-observation (EO) foundation models provide reusable embeddings, yet downstream task accuracy does not reveal whether these representations encode geographic information, which may be beneficial for location-aware applications but potentially detrimental when representations invariant to geogra…

• GeoRefer-Bench: A Benchmark from Referring Pixels to Verifiable Geospatial Reasoning

  Shuaishuai Cao, Min Huang, Meng Tang, Xuan Liu, Youjin Wang, Hui Lin · 25 de septiembre de 2026

  Referring segmentation in overhead imagery is inherently relational: a query may ask for the buildings north of the road or the pond closest to a residential area, so the correct referent can contain one object, several objects, or none. Existing benchmarks mainly score mask overlap, which cannot ve…

• MIND the Gap: A Geographic Implicit Neural Representation with Adjustable Spatial Scale

  Isaac Corley, Arjun Rao, Esther Rolf, Konstantin Klemmer, Evan Shelhamer, Nils Lehmann, Marc Ru{\ss}wurm, Gengchen Mai, Nathan Jacobs, Hannah Kerner · 23 de septiembre de 2026

  Geographic measurements are often sparse, leaving large areas without labels for the quantities we want to map. Geographic implicit neural representations (INRs) address this by learning smooth, general-purpose embeddings that can be queried at any coordinate. Downstream models combine these embeddi…

• LLM-Driven Training-free Location-Attribute Synergic Fusion: A Closed-Loop Paradigm for Dual-source Encrypted POIs and LULC Mapping

  Chang Li, Xingtao Peng, Yongjun Zhang, Yinfei He, Cairun Huang · 23 de septiembre de 2026

  Dual-source encrypted points of interest (DSEP), POIs from two encrypted coordinate systems, suffer from intertwined location and attribute uncertainties, including nonlinear systematic misalignment and naming inconsistency, hindering land-use/land-cover (LULC) mapping. To the best of our knowledge,…

• Analyzing Public Discourse on Urbanism: Topic Clustering, Sentiment Analysis and Retrieval-Augmented Generation using YouTube Comments

  Jakob Morales, Monica Hegde, Fayeq Jeelani Syed · 22 de septiembre de 2026

  Online discourse about urban issues - walkability, cycling infrastructure, public transit, housing density, and street safety - is voluminous but unstructured, and existing city-evaluation tools capture none of it. We present a pipeline and conversational system that combines geographic entity resol…

• Hiding in Plain Sight: A Diffusion-based Mitigation of Geolocation Privacy Leakage in Vision-Language Models

  Yining Wang, Xi Li, Mi Zhang, Xiaohan Zhang, Xiaoyu You, Zhenxing Qian, Mi Wen · 21 de septiembre de 2026

  Multimodal large reasoning models (MLRMs) have demonstrated remarkable capabilities in complex visual understanding. However, this very power introduces a critical yet underexplored privacy threat: adversaries can exploit MLRMs to precisely infer users' geographic locations from casually shared phot…

• CitySTAR: Structured and Topology-Aware Reasoning for Open-Vocabulary Urban 3D Grounding

  Shuai Zhang, Hongye Hou, Qinghe Liu, Zhuoxiao Li, Dongli Wu, Jing Ou, Yuan Liu, Wufan Zhao · 18 de septiembre de 2026

  3D grounding aims to localize target entities in complex scenes from natural language and plays a fundamental role in embodied perception and spatial reasoning. However, existing approaches mostly rely on feature similarity or direct matching, making it difficult to connect natural-language intent w…

• Constraint-Safe Graph-Context Scoring for Stable Point-Feature Labels Under Text-Width and Accessibility-Inspired Profiles

  Taimoor Ahmad · 18 de septiembre de 2026

  Point-feature label placement on interactive maps must reconcile geometric validity, display yield, local placement utility, and stability across camera motion. Accessibility and multilingual requirements further change label dimensions, yet algorithmic evaluations often collapse these concerns into…

• ANASSA: An Agentic AI Orchestration Framework for Spatial Intelligence

  Constantinos Papantoniou, Brian Hilton · 15 de septiembre de 2026

  The emergence of large language models (LLMs) and large multimodal models (LMMs) has enabled a new class of agentic systems capable of integrating natural language understanding with tool-based execution. In geographic information systems (GIS), this shift is transforming traditional, expert-driven …

• GeoSkill:Experience-Driven Hierarchical Skill Learning with Collaborative Revision forGeospatialAgents

  Han Luo, Xian Xu, Yinhe Liu, Yanfei Zhong · 15 de septiembre de 2026

  Geospatial agents are increasingly expected to support recurring and evolving analytical tasks rather than execute isolated workflows. In such settings, effective agents must distill prior execution experience into reusable geospatial procedural knowledge to guide future planning and tool use. Howev…

• Applying foundation model embeddings towards urban livability evaluation

  Ayush Khot, Wen Zhou, Shaowen Wang · 10 de septiembre de 2026

  While accurate measurement of socioeconomic indicators remains challenging in data-scarce regions, which limits policy interventions and resource allocation, high-resolution geospatial data is widely available and can contain information on various livability statistics. We investigate which physica…

• GeoContext: One Context Ladder, Two Failure Modes in Vision-Language Geolocation: Flat Reliance on User-Provided Location Context and False Confirmation of Location Claims

  Yifan Zhang, Kai Wang · 9 de septiembre de 2026

  Visual geolocation benchmarks typically ask a model where an image was captured without accounting for the location context that users often provide. We introduce GeoContext, a resource supporting two complementary tasks: GeoHint, open-ended localization given a true but coarse location hint, and Ge…

• Recovering Temporal and Geographic Signals from Language Model Embeddings

  Esteban Feuerstein, Victoria Klimkowski, Juan Manuel Ortiz de Zarate, Federico Hern\'an Suaiter · 9 de septiembre de 2026

  Understanding whether language-model embeddings encode structured real-world information is important for both representation analysis and information retrieval. We study this question for temporal and geographic signals using a simple projection-based method that operates directly on output embeddi…

• SimCRAFT: Distilling Remote Sensing Agents via Synthetic Trajectories and Contextual Retrieval-Augmented Fine-Tuning

  Haoran Wang, Jing Yao, Xu Yang, Zeqing Wang, Yang Zhang, Pedram Ghamisi, Zhengchao Chen · 7 de septiembre de 2026

  The unprecedented surge in Earth observation data volume and diversity has exposed a critical bottleneck for traditional manual workflows, catalyzing the emergence of Remote Sensing (RS) Agents. However, the practical deployment of these advanced agents is severely hindered by their heavy reliance o…

• IPGeoAI: Transformer-Based Geolocation with LLM Semantic Fusion

  Avinash Kadimisetty, Andy Jinqing Yu, Philip Favaloro, Wenlong Liu, Xiaolu Xiong · 7 de septiembre de 2026

  Accurate city-level IP Geolocation is an important enabler for the modern digital ecosystem, underpinning services ranging from local content delivery and targeting to digital rights enforcement. However, traditional heuristic and database-driven methods often struggle to resolve the complex, non-li…

• Urban Boundaries, Social Barriers: A Benchmark and Vision-Centric Framework for Mapping Gated Communities and Equity Implications

  Minwei Zhao, Weiming Zhang, Jiawang Du, Qiming Liu, Weiming Zhuang, Pei Nie, Cai Wu · 4 de septiembre de 2026

  Communities are fundamental spatial units that shape urban form and social life. Whether a residential compound is spatially open or enclosed affects mobility, access to public services, and equity, yet studies of Chinese fengbi xiaoqu remain largely qualitative or small-scale, limiting reproducible…

• From Open Standards to Openly Governed: Standards-Setting Organizations as Stewards of Openness amid Platformization and Digital Sovereignty

  Bego\~na G. Otero, Stefaan G. Verhulst · 3 de septiembre de 2026

  Open geospatial standards let data, services, and systems work across platforms. But openness is not just a property of specifications. It also depends on the institutions that produce them and the infrastructures in which they operate. Standards may function as digital public goods and, once embedd…

• Do Satellites See Commuters? A Critical Benchmark of Vision Foundation Models

  Ashiq Shukoor Iqbal, Wilson Wongso, Flora D. Salim · 2 de septiembre de 2026

  Satellite foundation models offer a globally available alternative to census data for commuting origin-destination (OD) generation, yet no study has systematically compared encoder paradigms within a single downstream pipeline. We ablate four satellite vision encoders: language-supervised (RemoteCLI…

• You Cannot Photograph the Same Street Twice: Reliability Limits in Vision-Language Measurement of Urban Change

  Kaizhen Tan · 2 de septiembre de 2026

  Vision-language models are increasingly used to measure urban change from repeated street-level imagery, but their longitudinal reliability is not well understood. We test how much a perception score can change when the street itself does not undergo substantial redevelopment. Using 4,648 consecutiv…

• Location-Aware Language Models via Secondary Embeddings

  Gokul Srinivasagan, Munir Georges · 2 de septiembre de 2026

  Pretrained transformer-based language models achieve strong performance across a wide range of NLP tasks but remain limited in encoding geo-locational semantics, leading to suboptimal representations of place names and spatial entities. In this work, we propose a lightweight, model-agnostic approach…

• GeoGR^2:Zero-Shot Geospatial Inference via Geostatistically-Guided Iterative Refinement with LLMs

  Jinfan Tang, Kunming Wu, Xieruifeng Gong, Yuya He, HuJie, Wu Junhui, Yuankai Wu · 2 de septiembre de 2026

  Standard large language model prompting treats geospatial inference as independent, instance-wise prediction, ignoring the fundamental spatial dependencies that govern geographic reality. Consequently, even advanced models struggle with spatial consistency and exhibit severe biases toward populous r…