Extracting rampart crater ejecta blankets is crucial for understanding impact crater formation and material transport processes, offering key insights into the distribution of subsurface water and ice on Mars. However, traditional methods often fail to extract rampart crater ejecta blankets due to complex terrain, noise interference, and blurred boundaries. To overcome these challenges, we propose an edge-aware segment anything model (SAM) sputter analysis (EASSA) framework for the rapid and accurate extraction of rampart crater ejecta contours. EASSA comprises three key components. First, Wiener filtering and multiscale Retinex preprocessing are applied to suppress terrain noise and enhance the visual distinction between rampart features and the background. Second, an SAM-based unsupervised segmentation module is employed to automatically identify rampart crater boundaries. Finally, we refined the extracted edges by designing a contour optimization pipeline that applies classical image operators, such as Sobel to enhance gradients, Suzuki–Abe to correct contours, and Douglas–Peucker to simplify and smooth shapes. To validate EASSA, we construct a multiscale rampart crater dataset using context camera (targeting craters 1 km) in the Chryse Planitia and Arabia Terra regions. Experimental results demonstrate that our method achieves a detection accuracy of 97.36%, a recall of 93.36%, and an intersection over union of 0.93, significantly outperforming the baseline SAM segmentation. Morphological analysis further reveals that rampart ejecta in both regions exhibit mobilities greater than 2, an average lobateness coefficient of 1.06, and relatively shallow excavation depths. Additionally, we observe that elevated terrains exhibit lower ejecta flow mobility under similar latitudinal conditions, while geomorphic evidence of past fluvial activity remains evident. These findings provide new insights into Martian subsurface water dynamics and the mobility characteristics of ejecta under varied geologic settings.

Achieving lightweight representations of building mesh models with accurate geometry and fine structural details is a key challenge in urban 3D modelling. Most existing mesh simplification methods focus on minimizing geometric error while neglecting the specific characteristics of building models in terms of geometric structure and semantic hierarchy, thus leading to structural degradation and semantic inconsistencies. To address this issue, this paper proposes a structure–semantic dual-constrained edge-collapse decimation method for simplifying dense building mesh models reconstructed from point clouds. Our core innovation lies in the joint enforcement of geometric structural constraints and building semantic constraints to effectively preserve both geometric structural features and component-level semantic structures of the models. By incorporating these two constraints, we adaptively assign higher collapse penalties to key structural edges and semantic boundaries, achieving lightweight building model simplification while maintaining fine-level structural details even under high compression ratios. Our method is extensively validated on several datasets of varying scales and complexities, including single-building models from Sketchfab, the large-scale urban datasets SUM and STPLS3D, and the ArCH cultural heritage dataset. Experimental results demonstrate that our method achieves superior or comparable performance compared to the existing methods across all the test datasets, consistently achieving lower or on-par geometric errors measured by RMSE and MAE. Furthermore, our simplified results can be semantically organized and stored under the CityGML paradigm, which provides a unified data support for sharing, semantic retrieval, downstream analysis, and other applications of lightweight building models.

Satellite remote sensing imagery has become an essential resource for large-scale surface water monitoring. Nevertheless, in karst regions, the elongated and fragmented morphology of water bodies, along with terrain shadows and vegetation interference, still leads to limitations in existing methods for small water body detection and accurate boundary delineation. To overcome the aforementioned issues, this paper proposes MESA-Net, a CNN–Mamba hybrid segmentation network for water body extraction in complex karst terrain. The network employs ResNet-18 as an encoder to extract shallow-level features. The decoder primarily consists of three modules: the Cross-Scale Adaptive Feature Fusion (CAFF) module, the Directional Gradient Histogram Edge-Guided Fusion (DGHEF) module, and the Omni-directional Global-Local Mamba Block (OGLMB). Among these, the CAFF module enhances the detection capability for small-scale water bodies by performing cross-scale feature fusion and dynamic weight allocation on the feature outputs from each level of the encoder. The OGLMB integrates an omnidirectional state space model with an 8-directional scanning mechanism and cross-attention guidance, effectively enhancing the ability to represent the structural continuity and global consistency of water bodies. The DGHEF utilizes directional gradient histograms to explicitly model multi-directional boundary information of water bodies, and combines this with a boundary guidance mechanism to enhance the representation of water body boundary features whilst suppressing spurious responses. In addition, the LJ-Water dataset has been constructed for the Lijiang River Basin in Guangxi, which is based on Sentinel-2 imagery. To validate the effectiveness and generalization capability of the method, comparative experiments were conducted on the self-built LJ-Water dataset as well as the publicly available Water-CD and LoveDA datasets. Experimental results demonstrate that MESA-Net consistently outperforms representative CNN-based, Transformer-based, and Mamba-based segmentation networks. On the LJ-Water dataset, it achieves 84.59% IoU and 91.65% F1, whilst on the Water-CD dataset, it attains 92.15% IoU and 95.91% F1, and 69.83% IoU and 82.24% F1 on the LoveDA dataset. Relative to the strongest baseline method, the proposed model achieved IoU gains of 1.51%, 2.34%, and 1.73% on the three datasets, respectively. In summary, MESA-Net demonstrates superior water segmentation performance under complex background conditions.