The paper introduces MV-PVMapper, a training‑free framework that combines vision foundation models (SAM 3) with UAV photogrammetry to produce high‑fidelity maps of photovoltaic modules. By generating candidate contours, correcting for surface slope, and iteratively refining segmentation across multiple views, the method recovers 7,710 of 7,711 modules with no false positives and achieves macro‑averaged precision and recall above 98 %. The approach outperforms orthomosaic segmentation and multi‑view pixel‑voting baselines, reaching a Jaccard index of 97.55 % without any task‑specific training data.
By WenHao Duan, Qi Chen, Penglei Zhang, Ruizhe Deng, Zhiling Guo, Haoran Zhang, Yuntian Chen
The paper introduces AeroBelief, a dual‑layer semantic‑spatial belief mapping framework for aerial object goal navigation. It separates broad contextual plausibility (intuition layer) from target‑specific evidence (evidence layer) and fuses them into persistent spatial belief hotspots. The method also employs object‑conditioned visual reasoning and egocentric regional guidance, achieving state‑of‑the‑art success rates on the UAV‑ON benchmark.
By Jianqiang Xiao, Xiang Deng, Yuexuan Sun, Yanjin Wu, Wenbiao Yan, Liqiang Nie
The paper introduces GeoReasoner, a framework for Aerial Vision-and-Language Navigation that replaces reactive trial-and-error with geospatial-guided active reasoning. It first uses pre-search geospatial reasoning to generate region-level priors and limit the goal hypothesis space, then performs hypothesis-driven exploration to gather discriminative evidence, and finally applies consistency-aware goal verification to avoid premature confirmations. Experiments on the CityNav benchmark show GeoReasoner achieves state‑of‑the‑art zero‑shot performance, rivaling supervised methods.
By Jiawei Ge, Wenkang Gong, Jiuxin Cao, Bo Liu, Lei Cui, Haoran Wang, Xuelin Zhu, Biwei Cao, Chang Liu, Chen Feng, Ioannis Patras
arXiv:2607. 22205v2 Announce Type: replace-cross Abstract: Remote sensing multimodal large language models (RS-MLLMs) have improved general aerial-image understanding.
By Yuheng Zong, Minghua Wang, Xin Zhao, Zhi-Hui Zhan, Antonio Plaza, Jon Atli Benediktsson
arXiv:2603. 04277v2 Announce Type: replace-cross Abstract: Autonomous aerial robots operating in GPS-denied or communication-degraded environments frequently lose access to camera metadata and telemetry, leaving onboard perception systems unable to recover the absolute metric scale of the scene.
By Yifei Chen, Chenqian Le, Jiayi Cheng, Xupeng Chen
The paper introduces AeroBelief, a dual‑layer semantic‑spatial belief mapping framework for aerial object goal navigation. It separates broad contextual plausibility (intuition layer) from target‑specific evidence (evidence layer) and fuses them into persistent spatial belief hotspots, while also employing object‑conditioned visual reasoning and temporally stable regional guidance. Experiments on the UAV‑ON benchmark show AeroBelief outperforms prior methods in success rate, object success rate, and SPL.
Drone-PUR is a new dataset and benchmark for vision‑language models that focuses on visual clue discovery, interpretation, and integration in UAV scenes. It contains 11 tasks with 175k training and 20k test samples across single images, image sequences, cross‑modal images, and multiple viewpoints, all of which are human‑reviewed. Evaluation of 22 general‑purpose VLMs shows persistent gaps in spatial judgment and cross‑modal/cross‑view reasoning, while fine‑tuning on Drone‑PUR improves accuracy by up to 18 percentage points.
By Jiahe Wu, Bing Cao, Xingyue Guo, Ruipu Zhao, Beiming Yu, Jiekang Feng, Qinghua Hu, Pengfei Zhu
AMIGO (Agentic Multi-Image Grounding Oracle Benchmark) is a long-horizon evaluation framework for vision‑language models that tests hidden‑target identification across galleries of visually similar images. The benchmark requires a model to ask a sequence of attribute‑focused Yes/No questions, receiving Yes/No/Unsure feedback and penalizing invalid actions with Skip, thereby stressing question selection under uncertainty, constraint tracking, and fine‑grained discrimination. Using the Guess My Preferred Dress task, the study shows that final‑answer accuracy alone overstates performance, as models may guess correctly without verified evidence, waste turns, or violate the protocol, highlighting the need for combined visual discrimination, informative questioning, and robust protocol adherence.
By Min Wang, Ata Mahjoubfar
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:2610.33056v1 Announce Type: cross
Abstract: While Multimodal Large Language Models (MLLMs) have revolutionized general visual perception, existing methods suffer from severe hallucination in th...
By Jisheng Dang, Huaiyuan Peng, Yixuan Wang, Kun Wang, Zhang Li, Tat-Seng Chua
GeoRefer-Bench is a new benchmark for verifiable geospatial referring segmentation that evaluates whether models correctly resolve spatial relations in overhead imagery. Each query is expressed as an executable logical form over a metric scene graph, and predictions are scored with Exact Query Success (EQS), requiring an exact match to the query’s referent set. The dataset contains 700 UAV scenes, 26,217 instances, 142,796 spatial relations, 20,916 executable queries across five reasoning levels, and additional paraphrases, unanswerable queries, counterfactual pairs, and leakage‑controlled splits.
By Shuaishuai Cao, Min Huang, Meng Tang, Xuan Liu, Youjin Wang, Hui Lin
The study evaluates the use of frozen geospatial foundation embeddings (AlphaEarth) for mapping cultivated versus non‑cultivated land in Maine. Using 192 spatially separated patches and USDA Cropland Data Layer labels, a lightweight classifier achieved 93.7% overall accuracy without fine‑tuning, and a nearest‑class‑centroid rule reached 90.2%. A balanced sample of 60,000 labeled pixels was nearly as effective as the full 8.6 million‑pixel pool, and classifiers trained in one year remained accurate across 2018‑2023. In a blind human validation of 385 points, the AlphaEarth‑plus‑random‑forest map matched 95.3% of the consensus, outperforming the CDL reference (91.7%).
By Mohammad Ammar Mughees, Giovanni Montefoschi, Zhongxin Chen, Maria Antonia Brovelli