Geospatial Chain of Thought Reasoning for Enhanced Visual Question Answering on Satellite Imagery

• URL: http://arxiv.org/abs/2511.11198v1
• Date: Fri, 14 Nov 2025 11:54:05 GMT
• Title: Geospatial Chain of Thought Reasoning for Enhanced Visual Question Answering on Satellite Imagery
• Abstract summary: Geospatial chain of thought (CoT) reasoning is essential for advancing Visual Question Answering (VQA) on satellite imagery.<n>We propose a VQA framework that integrates CoT reasoning with Direct Preference Optimization (DPO) to improve interpretability, robustness, and accuracy.<n> Experiments show that CoT supervision improves accuracy by 34.9% over direct baselines, while DPO yields additional gains in accuracy and reasoning quality.
• Score: 1.9153836153207706
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Geospatial chain of thought (CoT) reasoning is essential for advancing Visual Question Answering (VQA) on satellite imagery, particularly in climate related applications such as disaster monitoring, infrastructure risk assessment, urban resilience planning, and policy support. Existing VQA models enable scalable interpretation of remote sensing data but often lack the structured reasoning required for complex geospatial queries. We propose a VQA framework that integrates CoT reasoning with Direct Preference Optimization (DPO) to improve interpretability, robustness, and accuracy. By generating intermediate rationales, the model better handles tasks involving detection, classification, spatial relations, and comparative analysis, which are critical for reliable decision support in high stakes climate domains. Experiments show that CoT supervision improves accuracy by 34.9\% over direct baselines, while DPO yields additional gains in accuracy and reasoning quality. The resulting system advances VQA for multispectral Earth observation by enabling richer geospatial reasoning and more effective climate use cases.

Related papers

• Reinforcement Learning-Guided Evolutionary Policy Optimization for Preference-Adjustable Heterogeneous Agile Earth Observation Satellite Scheduling [50.176885352114674]
  Heterogeneous agile Earth observation satellite (AEOS) scheduling requires task selection, satellite assignment, and observation sequencing.<n>This paper proposes an evolutionary policy optimization framework for heterogeneous AEOS scheduling with preference-adjustable weighted objectives.
  arXiv  Detail & Related papers  (2026-08-25T12:16:28Z)
• GeoRisk-RAG: A Hierarchy-Aware Risk Framework for Improving RAG Reliability through Selective Answering [8.527283791363045]
  We present GeoRisk-RAG, a hierarchy-aware framework that addresses this geographic-validity gap through selective answering.<n>Experiments on a novel held-out wildfire-related question-answering dataset show that GeoRisk-RAG significantly reduces false confidence rates for location-dependent questions.<n>This work provides a more comprehensive assessment of end-to-end RAG pipelines by integrating geographic validity and selective-answering behavior for safer decision-making in geospatial domains.
  arXiv  Detail & Related papers  (2026-08-23T22:39:21Z)
• GCA Framework: A GCC Countries-Grounded Dataset and Agentic Pipeline for Climate Decision Support [67.9633930125889]
  We present the GCA framework, which unifies (i) GCA-DS, a curated multimodal dataset grounded in the GCC states, and (ii) Gulf Climate Agent (GCA), a tool-augmented agent for climate analysis.<n>GCA-DS comprises 200k question--answer pairs spanning governmental policies and adaptation plans, NGO and international frameworks, academic, literature, and event-driven reporting on heatwaves, dust storms, and floods.<n>GCA agent orchestrates a modular tool pipeline grounded in real-time and historical signals and geospatial processing that produces derived indices and interpretable visualizations.
  arXiv  Detail & Related papers  (2026-04-14T05:31:40Z)
• OpenEarthAgent: A Unified Framework for Tool-Augmented Geospatial Agents [68.85365034738534]
  We introduce a unified framework for developing tool-augmented geospatial agents trained on satellite imagery, natural-language queries, and detailed reasoning traces.<n>The training pipeline relies on supervised fine-tuning over structured reasoning trajectories, aligning the model with verified multistep tool interactions.<n>The accompanying corpus comprises 14,538 training and 1,169 evaluation instances, with more than 100K reasoning steps in the training split and over 7K reasoning steps in the evaluation split.
  arXiv  Detail & Related papers  (2026-02-19T18:59:54Z)
• Agentic Spatio-Temporal Grounding via Collaborative Reasoning [80.83158605034465]
  Temporal Video Grounding aims to retrieve thetemporal tube of a target object or person in a video given a text query.<n>We propose the Agentic Spatio-Temporal Grounder (ASTG) framework for the task of STVG towards an open-world and training-free scenario.<n>Specifically, two specialized agents SRA (Spatial Reasoning Agent) and TRA (Temporal Reasoning Agent) constructed leveraging on modern Multimoal Large Language Models (MLLMs)<n>Experiments on popular benchmarks demonstrate the superiority of the proposed approach where it outperforms existing weakly-supervised and zero-shot approaches by a margin
  arXiv  Detail & Related papers  (2026-02-10T10:16:27Z)
• Agentic AI Framework for Cloudburst Prediction and Coordinated Response [0.8697317909540486]
  The paper outlines an agentic artificial intelligence system to study atmospheric water-cycle intelligence.<n>The framework uses autonomous but cooperative agents that reason, sense, and act throughout the entire event lifecycle.<n>It provides a platform of scalable adaptive and learning-based climate resilience.
  arXiv  Detail & Related papers  (2025-11-27T21:33:03Z)
• Think First, Assign Next (ThiFAN-VQA): A Two-stage Chain-of-Thought Framework for Post-Disaster Damage Assessment [0.0]
  We propose ThiFAN-VQA, a two-stage reasoning-based framework for visual question answering (VQA) in disaster scenarios.<n>By integrating a custom information retrieval system, domain-specific prompting, and reasoning-guided answer selection, ThiFAN-VQA bridges the gap between zero-shot and supervised methods.<n> Experiments on FloodNet and RescueNet-VQA, UAV-based datasets from flood- and hurricane-affected regions, demonstrate that ThiFAN-VQA achieves superior accuracy, interpretability, and adaptability.
  arXiv  Detail & Related papers  (2025-11-24T14:32:07Z)
• Security-Aware Joint Sensing, Communication, and Computing Optimization in Low Altitude Wireless Networks [83.84711311344918]
  Integrated sensing, communications, and computing (I SCC) is one of the core parts of Low-Altitude Wireless Networks (LAWNs)<n>This paper studies joint performance optimization of I SCC while considering secrecyness of the communications.<n>We propose a deep Q-network (DQN)-based multi-objective evolutionary algorithm, which adaptively selects evolutionary operators according to the evolving optimization objectives.
  arXiv  Detail & Related papers  (2025-11-03T11:06:41Z)
• Towards Faithful Reasoning in Remote Sensing: A Perceptually-Grounded GeoSpatial Chain-of-Thought for Vision-Language Models [8.021952962029165]
  Vision-Language Models (VLMs) in remote sensing often fail at complex analytical tasks.<n>We introduce the Perceptually-Grounded Geospatial Chain-of-Thought (Geo-CoT)<n>Geo-CoT is a framework that models remote sensing analysis as a verifiable, multi-step process.
  arXiv  Detail & Related papers  (2025-09-26T11:34:42Z)
• GeoSR: Cognitive-Agentic Framework for Probing Geospatial Knowledge Boundaries via Iterative Self-Refinement [4.026524042818433]
  GeoSR is a self-refining agentic reasoning framework that embeds core geographic principles into an iterative prediction loop.<n>We validate GeoSR on tasks ranging from physical-world property estimation to socioeconomic prediction.
  arXiv  Detail & Related papers  (2025-08-06T04:45:34Z)
• SVQA-R1: Reinforcing Spatial Reasoning in MLLMs via View-Consistent Reward Optimization [57.484274282231226]
  We propose SVQA-R1, the first framework to extend R1-style training to spatial VQA.<n>In particular, we introduce Spatial-GRPO, a novel group-wise RL strategy that constructs view-consistent rewards by perturbing spatial relations between objects.<n>Our model, SVQA-R1, not only dramatically improved accuracy on spatial VQA benchmarks but also exhibits interpretable reasoning paths even without using supervised fine-tuning data.
  arXiv  Detail & Related papers  (2025-06-02T06:58:43Z)
• Leveraging Land Cover Priors for Isoprene Emission Super-Resolution [15.868193361155656]
  This study contributes to atmospheric chemistry and climate modeling by providing a cost-effective, data-driven approach to refining BVOC emission maps.<n>The proposed method enhances the usability of satellite-based emissions data, supporting applications in air quality forecasting, climate impact assessments, and environmental studies.
  arXiv  Detail & Related papers  (2025-03-24T13:23:46Z)
• Spatial-RAG: Spatial Retrieval Augmented Generation for Real-World Geospatial Reasoning Questions [5.053463027769152]
  Spatial-RAG is a Retrieval-Augmented Generation framework designed for geospatial question answering.<n>It integrates structured spatial databases with large language models (LLMs) via a hybrid spatial retriever.<n>It formulates the answering process as a multi-objective optimization over spatial and semantic relevance.
  arXiv  Detail & Related papers  (2025-02-04T01:30:06Z)
• Long-term drought prediction using deep neural networks based on geospatial weather data [75.38539438000072]
  High-quality drought forecasting up to a year in advance is critical for agriculture planning and insurance. We tackle drought data by introducing an end-to-end approach that adopts a systematic end-to-end approach. Key findings are the exceptional performance of a Transformer model, EarthFormer, in making accurate short-term (up to six months) forecasts.
  arXiv  Detail & Related papers  (2023-09-12T13:28:06Z)
• Deep generative model super-resolves spatially correlated multiregional climate data [5.678539713361703]
  We show an adversarial network-based machine learning enables us to correctly reconstruct the inter-regional spatial correlations in downscaling. The proposed method has a potential application to the inter-regionally consistent assessment of the climate change impact. We present the outcomes of another variant of the deep generative model-based downscaling approach in which the low-resolution precipitation field is substituted with the pressure field.
  arXiv  Detail & Related papers  (2022-09-26T05:45:16Z)
• Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarms [53.38353133198842]
  Air quality significantly affects human health, it is increasingly important to accurately and timely predict the Air Quality Index (AQI) This paper proposes a new federated learning-based aerial-ground air quality sensing framework for fine-grained 3D air quality monitoring and forecasting. For ground sensing systems, we propose a Graph Convolutional neural network-based Long Short-Term Memory (GC-LSTM) model to achieve accurate, real-time and future AQI inference.
  arXiv  Detail & Related papers  (2020-07-23T13:32:47Z)
• Bottom-up mechanism and improved contract net protocol for the dynamic task planning of heterogeneous Earth observation resources [61.75759893720484]
  Earth observation resources are becoming increasingly indispensable in disaster relief, damage assessment and related domains. Many unpredicted factors, such as the change of observation task requirements, to the occurring of bad weather and resource failures, may cause the scheduled observation scheme to become infeasible. A bottom-up distributed coordinated framework together with an improved contract net are proposed to facilitate the dynamic task replanning for heterogeneous Earth observation resources.
  arXiv  Detail & Related papers  (2020-07-13T03:51:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.