When performing remote sensing image segmentation, practitioners often encounter various challenges, such as a strong imbalance in the foreground–background, the presence of tiny objects, high object density, intra-class heterogeneity, and inter-class homogeneity. To overcome these challenges, this paper introduces AerialFormer, a hybrid model that strategically combines the strengths of Transformers and Convolutional Neural Networks (CNNs). AerialFormer features a CNN Stem module integrated to preserve low-level and high-resolution features, enhancing the model’s capability to process details of aerial imagery. The proposed AerialFormer is designed with a hierarchical structure, in which a Transformer encoder generates multi-scale features and a multi-dilated CNN (MDC) decoder aggregates the information from the multi-scale inputs. As a result, information is taken into account in both local and global contexts, so that powerful representations and high-resolution segmentation can be achieved. The proposed AerialFormer was benchmarked on three benchmark datasets, including iSAID, LoveDA, and Potsdam. Comprehensive experiments and extensive ablation studies show that the proposed AerialFormer remarkably outperforms state-of-the-art methods.

As climate change intensifies, the global imperative to shift towards sustainable energy sources becomes more pronounced. Photovoltaic (PV) energy is a favored choice due to its reliability and ease of installation. Accurate mapping of PV installations is crucial for understanding their adoption and informing energy policy. To meet this need, we introduce the SolarFormer, designed to segment solar panels from aerial imagery, offering insights into their location and size. However, solar panel identification in Computer Vision is intricate due to various factors like weather conditions, roof conditions, and Ground Sampling Distance (GSD) variations. To tackle these complexities, we present the SolarFormer, featuring a multi-scale Transformer encoder and a masked-attention Transformer decoder. Our model leverages low-level features and incorporates an instance query mechanism to enhance the localization of solar PV installations. We rigorously evaluated our SolarFormer using diverse datasets, including GGE (France), IGN (France), and USGS (California, USA), across different GSDs. Our extensive experiments consistently demonstrate that our model either matches or surpasses state-of-the-art models, promising enhanced solar panel segmentation for global sustainable energy initiatives.

Aerial Image Segmentation is a top-down perspective semantic segmentation and has several challenging characteristics such as strong imbalance in the foreground-background distribution, complex background, intra-class heterogeneity, inter-class homogeneity, and tiny objects. To handle these problems, we inherit the advantages of Transformers and propose AerialFormer, which unifies Transformers at the contracting path with lightweight Multi-Dilated Convolutional Neural Networks (MD-CNNs) at the expanding path. Our AerialFormer is designed as a hierarchical structure, in which Transformer encoder outputs multi-scale features and MD-CNNs decoder aggregates information from the multi-scales. Thus, it takes both local and global contexts into consideration to render powerful representations and high-resolution segmentation. We have benchmarked AerialFormer on three common datasets including iSAID, LoveDA, and Potsdam. Comprehensive experiments and extensive ablation studies show that our proposed AerialFormer outperforms previous state-of-the-art methods with remarkable performance. Our source code will be publicly available upon acceptance.

Vision-Language-Action (VLA) models adapt pretrained Vision-Language Models (VLMs) for robotic control but often bind instructions to incorrect visual evidence. Single-object tests show that state-of-the-art policies grasp the visible object even when the requested referent is absent. We ask whether explicit grounding can improve instruction-scene alignment under visual distraction and distribution shift without distractor-rich demonstrations or auxiliary perceptual objectives. We propose Distraction-Robust Action via Geometric and Object-ceNtric Grounding (DRAGON-VLA), an integrated perception-control system. Its two-stage object-centric module uses an off-the-shelf VLM to select segmentation proposals in a base view and match them semantically in a wrist view. Its geometric module maps selected regions to masked monocular relative depth, preserving shape and layout while suppressing appearance, background, and distractors. These inputs condition a pretrained VLA fine-tuned only on clean, single-object demonstrations. DRAGON-VLA offers a task-aware, calibration-free interface combining base-view anchoring, cross-view matching, and depth grounding without additional high-level grounding supervision. On a real UR10e tabletop setup, it outperforms VLA baselines under distractors, absent referents, background shifts, and held-out categories. With seven distractors, grounded policies retain around 80% task success while baselines fall below 10%; they also achieve approximately 95% absent-target rejection. Ablations identify object-centric filtering as the main source of gains, with masked relative depth providing its clearest benefit on unseen objects. These results support explicit grounding for referentially reliable VLA manipulation. Project page: https://uark-aicv.github.io/DRAGON_VLA/.