Multispectral aerial object detection aims to exploit spectral-spatial cues to address object detection challenges in complex aerial scenes. Existing methods mainly focus on spectral-spatial decouplin...
arXiv:2509.23056v3 Announce Type: replace Abstract: Remote sensing object detection is a critical technology for real-world applications such as natural resource monitoring, traffic management, and U...
arXiv:2606. 30576v1 Announce Type: cross Abstract: Cross-view object geo-localization (CVOGL) aims to locate a target object from a query view (e.
arXiv:2610.27882v1 Announce Type: new Abstract: Multispectral object detection requires detectors to exploit material-dependent spectral responses while maintaining consistent localization under geom...
LiG-DETR introduces a Global-Local Reassembly framework for aerial object detection that captures high‑fidelity local features before compression and integrates them into a unified end‑to‑end DETR decoder. The method uses a shared encoder to extract both global and locally magnified features, reassembles the local features according to their spatial positions, and employs Context‑Preserved Selective Reassembly and Density‑Aware Adaptive Query Allocation to reduce redundant computation. Experiments demonstrate significant improvements on small and medium objects while maintaining strong performance on large objects, with favorable accuracy–efficiency trade‑offs and better cross‑domain generalization.
M3GA-Wild is a new benchmark for multi-modal, multi-session ground-to-aerial place recognition in forests, combining synchronized RGB imagery and LiDAR from ground traversals with high‑resolution aerial imagery and multi‑altitude LiDAR over 370 hectares. The dataset includes accurate geo‑referenced 6‑DoF poses and spans 36 km of forest traversals, enabling systematic evaluation of visual, LiDAR, cross‑modal, and multi‑modal methods. Baseline experiments show LiDAR outperforms vision‑only approaches under severe viewpoint changes, while current multi‑modal fusion offers limited gains due to poor cross‑modal alignment, highlighting challenges in cross‑platform localisation and domain gaps.
HyperVision introduces the first ground‑based hyperspectral pre‑trained backbone, addressing challenges of varying spectral configurations, limited annotations, and dataset diversity. It employs a channel‑adaptive dynamic embedding to unify heterogeneous inputs, a multi‑source pseudo‑labeling strategy combining SAM2 spatial cues with HyperFree spectral details, and cross‑modal knowledge distillation from a pre‑trained RGB vision model. Trained on 15k images from 26 datasets, HyperVision achieves significant improvements—up to 16.3% relative gain in hyperspectral semantic segmentation, 2.1% in object tracking AUC, and 35.5% reduction in salient object detection MAE—while requiring only head‑only adaptation.
arXiv:2603.08491v3 Announce Type: replace Abstract: Cross-modal Geo-localization (CMGL) matches ground-level text descriptions with geo-tagged aerial imagery, which is crucial for pedestrian navigati...
We present M3GA-Wild, the first benchmark for multi-modal, multi-session ground-to-aerial place recognition in forests. M3GA-Wild unifies and extends existing forest localisation datasets, providing a...
arXiv:2608. 09270v1 Announce Type: cross Abstract: Fine-grained cross-modal understanding in drone views is essential for aerial vision-language navigation.
FLORO is a multimodal geospatial foundation model that learns transferable representations from a small but diverse remote sensing corpus, including Sentinel-1, Sentinel-2, SkySAT imagery, elevation, and UAV data. It uses masked autoencoding and availability‑aware inputs to handle varying sensor configurations, enabling a unified input space across heterogeneous modalities. Evaluated on the PANGAEA benchmark, FLORO achieved strong, stable transfer performance across scene classification, segmentation, and regression tasks, ranking second in segmentation and third in scene classification while excelling in regression tasks.
DXPR is a depth‑based cross‑modal place recognition framework that matches monocular camera queries to a LiDAR map using a single vision foundation model backbone. By converting both modalities into a unified depth image representation, DXPR learns modality‑invariant global descriptors without modality‑specific encoders. A geometry‑aware overlap miner refines pairwise metric learning by computing pixel‑level overlap scores, and extensive tests on KITTI and Boreas show strong performance across seasons, weather, and day/night conditions, outperforming prior CMPR baselines.