{"total":51,"rows":[{"id":"2506.03135","title":"OmniSpatial: Towards Comprehensive Spatial Reasoning Benchmark for Vision Language Models","created":"2025-06-03","cited_by_count":114,"area":"lr"},{"id":"2509.18905","title":"How Far are VLMs from Visual Spatial Intelligence? A Benchmark-Driven Perspective","created":"2025-09-23","cited_by_count":43,"area":"lr"},{"id":"2503.07588","title":"When Large Vision-Language Model Meets Large Remote Sensing Imagery: Coarse-to-Fine Text-Guided Token Pruning","created":"2025-03-10","cited_by_count":35,"area":"mllm"},{"id":"2505.14361","title":"Vision-Language Modeling Meets Remote Sensing: Models, Datasets and Perspectives","created":"2025-05-20","cited_by_count":18,"area":"mllm"},{"id":"2506.01667","title":"EarthMind: Leveraging Cross-Sensor Data for Advanced Earth Observation Interpretation with a Unified Multimodal LLM","created":"2025-06-02","cited_by_count":18,"area":"mllm"},{"id":"2504.12110","title":"Towards LLM Agents for Earth Observation","created":"2025-04-16","cited_by_count":17,"area":null},{"id":"2604.02486","title":"VLMs Need Words: Vision Language Models Ignore Visual Detail In Favor of Semantic Anchors","created":"2026-04-02","cited_by_count":13,"area":"mllm"},{"id":"2512.17319","title":"A Benchmark for Ultra-High-Resolution Remote Sensing MLLMs","created":"2025-12-19","cited_by_count":11,"area":"lr"},{"id":"2509.05881","title":"GeoAnalystBench: A GeoAI benchmark for assessing large language models for spatial analysis workflow and code generation","created":"2025-09-07","cited_by_count":10,"area":null},{"id":"2512.20013","title":"SegEarth-R2: Towards Comprehensive Language-guided Segmentation for Remote Sensing Images","created":"2025-12-23","cited_by_count":10,"area":null},{"id":"2603.22148","title":"OpenEarth-Agent: From Tool Calling to Tool Creation for Open-Environment Earth Observation","created":"2026-03-23","cited_by_count":9,"area":"agents"},{"id":"2603.19039","title":"TerraScope: Pixel-Grounded Visual Reasoning for Earth Observation","created":"2026-03-19","cited_by_count":7,"area":"lr"},{"id":"2602.07045","title":"VLRS-Bench: A Vision-Language Reasoning Benchmark for Remote Sensing","created":"2026-02-04","cited_by_count":6,"area":"lr"},{"id":"2604.08896","title":"GeoMMBench and GeoMMAgent: Toward Expert-Level Multimodal Intelligence in Geoscience and Remote Sensing","created":"2026-04-10","cited_by_count":6,"area":"mllm"},{"id":"2506.12958","title":"Domain Specific Benchmarks for Evaluating Multimodal Large Language Models","created":"2025-06-15","cited_by_count":3,"area":"mllm"},{"id":"2603.09471","title":"OmniEarth: A Benchmark for Evaluating Vision-Language Models in Geospatial Tasks","created":"2026-03-10","cited_by_count":3,"area":"lr"},{"id":"2603.11804","title":"OSMDA: OpenStreetMap-based Domain Adaptation for Remote Sensing VLMs","created":"2026-03-12","cited_by_count":3,"area":"mllm"},{"id":"2503.09348","title":"MOAT: Evaluating LMMs for Capability Integration and Instruction Grounding","created":"2025-03-12","cited_by_count":2,"area":"mllm"},{"id":"2604.24919","title":"Agentic AI for Remote Sensing: Technical Challenges and Research Directions","created":"2026-04-27","cited_by_count":2,"area":"agents"},{"id":"2606.20177","title":"Evaluating and Enhancing Negation Comprehension in Remote Sensing MLLMs","created":"2026-06-18","cited_by_count":2,"area":"mllm"},{"id":"2501.14546","title":"Leveraging ChatGPT's Multimodal Vision Capabilities to Rank Satellite Images by Poverty Level: Advancing Tools for Social Science Research","created":"2025-01-24","cited_by_count":1,"area":null},{"id":"2504.13310","title":"SAR Object Detection with Self-Supervised Pretraining and Curriculum-Aware Sampling","created":"2025-04-17","cited_by_count":1,"area":null},{"id":"2505.10714","title":"GeoGrid-Bench: Can Foundation Models Understand Multimodal Gridded Geo-Spatial Data?","created":"2025-05-15","cited_by_count":1,"area":null},{"id":"2508.06832","title":"Remote Sensing Image Intelligent Interpretation with the Language-Centered Perspective: Principles, Methods and Challenges","created":"2025-08-09","cited_by_count":1,"area":null},{"id":"2512.07276","title":"Geo3DVQA: Evaluating Vision-Language Models for 3D Geospatial Reasoning from Aerial Imagery","created":"2025-12-08","cited_by_count":1,"area":"lr"},{"id":"2601.13401","title":"Reasoning with Pixel-level Precision: QVLM Architecture and SQuID Dataset for Quantitative Geospatial Analytics","created":"2026-01-19","cited_by_count":1,"area":"lr"},{"id":"2601.13839","title":"DisasterVQA: A Visual Question Answering Benchmark Dataset for Disaster Scenes","created":"2026-01-20","cited_by_count":1,"area":"mllm"},{"id":"2602.19615","title":"Seeing Clearly, Reasoning Confidently: Plug-and-Play Remedies for Vision Language Model Blindness","created":"2026-02-23","cited_by_count":1,"area":"lr"},{"id":"2605.12237","title":"UHR-Micro: Diagnosing and Mitigating the Resolution Illusion in Earth Observation VLMs","created":"2026-05-12","cited_by_count":1,"area":"mllm"},{"id":"2605.20006","title":"GeoX: Mastering Geospatial Reasoning Through Self-Play and Verifiable Rewards","created":"2026-05-19","cited_by_count":1,"area":"lr"},{"id":"2606.00987","title":"An Open-Source Benchmark and Baseline for Multi-temporal Referring Segmentation","created":"2026-05-31","cited_by_count":1,"area":"lr"},{"id":"2608.04244","title":"SIGNPOST-Bench: Benchmarking Text-Vision Conflict Resolution in Multimodal Large Language Models","created":"2026-08-04","cited_by_count":1,"area":"mllm"},{"id":"2609.27948","title":"VIVAS: Vitalizing Visual Perception in VLM Pre-training via Vision-language Unified Autoregressive Supervision","created":"2026-08-25","cited_by_count":1,"area":"mllm"},{"id":"2602.10771","title":"From Steering to Pedalling: Do Autonomous Driving VLMs Generalize to Cyclist-Assistive Spatial Perception and Planning?","created":"2026-02-11","cited_by_count":0,"area":"driving"},{"id":"2602.14017","title":"S2SServiceBench: A Multimodal Benchmark for Last-Mile S2S Climate Services","created":"2026-02-15","cited_by_count":0,"area":null},{"id":"2602.14989","title":"ThermEval: A Structured Benchmark for Evaluation of Vision-Language Models on Thermal Imagery","created":"2026-02-16","cited_by_count":0,"area":"mllm"},{"id":"2603.06687","title":"TimeSpot: Benchmarking Geo-Temporal Understanding in Vision-Language Models in Real-World Settings","created":"2026-03-04","cited_by_count":0,"area":"wm"},{"id":"2603.20305","title":"The Global-Local loop: what is missing in bridging the gap between geospatial data from numerous communities?","created":"2026-03-19","cited_by_count":0,"area":null},{"id":"2605.21919","title":"SDGBiasBench: Benchmarking and Mitigating Vision--Language Models' Biases in Sustainable Development Goals","created":"2026-05-21","cited_by_count":0,"area":null},{"id":"2605.25784","title":"Beyond Appearance: Can Multimodal Large Language Models Exploit Vertical Structure for Remote Sensing Natural Scene Understanding?","created":"2026-05-25","cited_by_count":0,"area":"mllm"},{"id":"2606.04046","title":"Dive into the Scene: Breaking the Perceptual Bottleneck in Vision-Language Decision Making via Focus Plan Generation","created":"2026-06-02","cited_by_count":0,"area":"vla"},{"id":"2607.06552","title":"MonoIR-RS: Infrared Remote Sensing Vision-Language Learning with CLIP and VLM Adaptation","created":"2026-07-07","cited_by_count":0,"area":null},{"id":"2607.15942","title":"More with Less: a Large Scale Remote Sensing VLM with a Simple Recipe","created":"2026-07-17","cited_by_count":0,"area":"mllm"},{"id":"2607.24810","title":"RRS-10K: A Multitask Vision-Language Model Benchmark for Rare Remote Sensing Image Interpretation","created":"2026-07-13","cited_by_count":0,"area":"mllm"},{"id":"2608.23525","title":"EarthVerse: Benchmarking Scientific Agents Across Dynamic Earth Systems and Natural Hazards","created":"2026-08-24","cited_by_count":0,"area":null},{"id":"2608.28567","title":"GeBDA: Building Damage Assessment as Text-Based Sequence Prediction","created":"2026-08-28","cited_by_count":0,"area":null},{"id":"2609.09790","title":"LogiScope-VQA: Benchmarking Vision-Language Models for Logistics Hazard Identification in Industrial Scenarios","created":"2026-09-09","cited_by_count":0,"area":"lr"},{"id":"2609.12533","title":"Earth-Agent-Pro: Towards Real-World Full-Chain Earth Observation with Agents","created":"2026-09-11","cited_by_count":0,"area":"agents"},{"id":"2609.26097","title":"One Domain, Many Tongues: Composing Domain and Language LoRAs for Cross-Lingual Remote-Sensing MLLMs without Paired Data","created":"2026-08-04","cited_by_count":0,"area":"mllm"},{"id":"2609.32813","title":"USAI-Quant: A Quantitative Reasoning Benchmark for Vision-Language Models in Built Environments","created":"2026-09-26","cited_by_count":0,"area":"lr"},{"id":"2610.19102","title":"The Gap Is in the Geometry: A Convergent Meta-Analysis of Multi-Facet Aerial and Remote Sensing Video Understanding","created":"2026-10-13","cited_by_count":0,"area":"mllm"}],"next_cursor":null}