GeoNLI introduces a unified, modular pipeline that combines advanced SAM variants with multimodal large language models to perform satellite image captioning, visual question answering (VQA), and visual grounding. The EarthMind model achieves strong results on captioning and VQA, while multiple RemoteSAM-SAM and DiffuSAM pipelines are used for grounding, ultimately employing a majority‑voting ensemble across several models. The system reports 82% captioning accuracy, 83.32% VQA accuracy, and 64.94% grounding accuracy, demonstrating improved consistency over task‑specific approaches.
By Ashutosh Gandhe, Anupam Rawat, Geet Sethi, Kabir Nasiruddin, Madhav Kotecha, Panav Shah, Rakshit Sawarn, Soumitra Nayak
arXiv:2606. 18271v1 Announce Type: new Abstract: As Earth Observation data generation outpaces downlink bandwidth and human-in-the-loop processing, a widening gap has emerged between onboard collection and actionable ground intelligence.
By Juan Manuel Delfa Victoria, Taran Cyriac John, Andrew W. Herson
arXiv:2512. 15231v3 Announce Type: replace Abstract: The automated and intelligent processing of massive remote sensing (RS) datasets is critical in Earth observation (EO).
By Zhengchao Chen, Haoran Wang, Jing Yao, Jianshe Zhang, Pedram Ghamisi, Jun Zhou, Peter M. Atkinson, Bing Zhang
arXiv:2608.30672v1 Announce Type: new
Abstract: Recent advances in large language models and multimodal models have pushed remote sensing (RS) processing from simple perception models to agentic syst...
By Boyang Mu, Zhiwei Wei, Mugen Peng, Wenjia Xu
The paper introduces TerraAgent, a training‑free framework that enhances remote sensing visual question answering by integrating three modules: GDR for demonstration retrieval, MACE for evidence‑typed sub‑question decomposition, and CVA for counterfactual evidence auditing. It demonstrates significant improvements on multiple benchmarks, raising accuracy, visual‑dependency scores, and reducing prior‑only answers and hallucinations without fine‑tuning.
By Jingwen Huang, Yiming Fang, Hanxiao Wu, Boyuan He, Keyi Luo
arXiv:2609.12533v1 Announce Type: cross
Abstract: Real-world Earth observation (EO) agents must translate high-level scientific questions into executable workflows to acquire observations, prepare da...
By Zhutao Lv, Chenhao Dang, Yi Feng, Yanpei Gong, Xiaolei Wang, Junyan Ye, Conghui He, Weijia Li
Geographic Information System (GIS) professionals rely on multi-step spatial analysis workflows to support decision-making in urban planning, disaster response, and environmental monitoring. The process is tedious, time-consuming, and error-prone.
RSure-Agent is a framework designed to improve remote sensing agents by verifying tool observations before they influence downstream reasoning. It introduces a verifiable observation protocol that requires tools to provide process evidence, and builds a task‑tool reliability prior from offline feedback to assess each tool configuration’s past performance. In evaluations on EarthBench, ThinkGeo, TerraLogic, and CHOICE‑420, RSure-Agent cuts error propagation by 21.3–25.9 percentage points and boosts overall accuracy, while also reducing the number of tool calls.
By Fuyuan Liu, Nayu Liu, Wenhao Yu, Peijin Wang, Yingchao Feng, Fanglong Yao, Yue Zhou, Xue Yang, Chao Ren, Liang Wan, Wei Feng
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. Howev...
The paper introduces PTC-Decoder, a training‑free, plug‑and‑play decoder designed to improve the reliability of small language models (SLMs) on offline, resource‑constrained edge devices such as remote sensing satellites. PTC-Decoder combines a Plan‑to‑Act paradigm that forces the model to invoke a planning tool at the first inference step with a deterministic finite automaton (TC‑Decoder) that imposes token‑level hard constraints on tool names while preserving the model’s reasoning capability. Evaluated on 200 real satellite tasks across seven SLMs, the approach achieves a statistically significant mean overall score gain of +1.21, with the TC‑Decoder component identified as the primary contributor to performance gains.
By Minghui Yu, Ke Mu, Gang Wu
GlobeBench is a new benchmark for evaluating text-based world models, comprising 1,632 tasks across five domains and nineteen simulation skills. It tests whether models can predict an environment’s response to an action based on the full agent trajectory, requiring understanding of state changes, environment rules, and action effects on related and unrelated objects. The benchmark compares predictions to execution-derived references using a hybrid architecture of deterministic gates and a semantic judge, and finds that GPT‑6 Astra tops the 20 tested models with a 49.57% success rate, while accuracy drops sharply when actions affect many dependent objects, leaving 631 tasks unsolved by any model.
By Nicholas Saban, Charvi Bannur, Mariya Vasileva, Darshan Deshpande
arXiv:2609.00814v1 Announce Type: new
Abstract: Remote sensing visual models have continuously advanced various interpretation tasks. However, the research process behind model improvement still heav...
By Kaiyue Kang, Qixuan He, Peijin Wang, Yingchao Feng, Chao Ren, Kangxin Wang, Wenhui Diao, Yixiao Wang, Liangjin Zhao, Kaiwen Wei, Nayu Liu, Xian Sun