RANdom SAmple Consensus (RANSAC) is a widely adopted method for LiDAR point cloud segmentation because of its robustness to noise and outliers. However, RANSAC has a tendency to generate false segments consisting of points from several nearly coplanar surfaces. To address this problem, we formulate the weighted RANSAC approach for the purpose of point cloud segmentation. In our proposed solution, the hard threshold voting function which considers both the point-plane distance and the normal vector consistency is transformed into a soft threshold voting function based on two weight functions. To improve weighted RANSAC’s ability to distinguish planes, we designed the weight functions according to the difference in the error distribution between the proper and improper plane hypotheses, based on which an outlier suppression ratio was also defined. Using the ratio, a thorough comparison was conducted between these different weight functions to determine the best performing function. The selected weight function was then compared to the existing weighted RANSAC methods, the original RANSAC, and a representative region growing (RG) method. Experiments with two airborne LiDAR datasets of varying densities show that the various weighted methods can improve the segmentation quality differently, but the dedicated designed weight functions can significantly improve the segmentation accuracy and the topology correctness. Moreover, its robustness is much better when compared to the RG method.

Outlier removal is a crucial step in local feature-based unmanned aerial vehicle (UAV) image matching. Inspired by our previous work, this paper proposes a method for reliable and efficient outlier removal in UAV image matching. The inputs of the method are only two images without any other auxiliary data. The core idea is to design local geometric constraints within the neighboring structure via the Delaunay triangulation and use a two-stage method for outlier removal and match refinement. In the filter stage, initial matches are first organized as the Delaunay triangulation (DT) and its corresponding graph, and their dissimilarity scores are computed from the affine-invariant spatial angular order (SAO), which is used to achieve hierarchical outlier removal. In addition, by using the triangle constraint between the refined Delaunay triangulation and its corresponding graph, missed inliers are resumed from match expansion. In the verification stage, retained matches are refined using the RANSAC-based global geometric constraint. Therefore, the two-stage algorithm is termed DTSAO-RANSAC. Finally, using four datasets, DTSAO-RANSAC is comprehensively analyzed and compared with other methods in feature matching and image orientation tests. The experimental results demonstrate that compared with the LO-RANSAC algorithm, DTSAO-RANSAC can achieve efficient outlier removal with speedup ratios ranging from 4 to 16 and, it can provide reliable matching results for image orientation of UAV datasets.

The identification and representation of building roof topology are basic, but important, issues for 3D building model reconstruction from point clouds. Always, the roof topology is expressed by the roof topology graph (RTG), which stores the plane–plane adjacencies as graph edges. As the decision of the graph edges is often based on local statistics between adjacent planes, topology errors can be easily produced because of noise, lack of data, and resulting errors in pre-processing steps. In this work, the hierarchical roof topology tree (HRTT) is proposed, instead of traditional RTG, to represent the topology relationships among different roof elements. Building primitives or child structures are taken as inside tree nodes; thus, the plane–model and model–model relations can be well described and further exploited. Integral constraints and extra verifying procedures can also be easily introduced to improve the topology quality. As for the basic plane-to-plane adjacencies, we no longer decide all connections at the same time, but rather we decide the robust ones preferentially. Those robust connections will separate the whole model into simpler components step-by-step and produce the basic semantic information for the identification of ambiguous ones. In this way, the effects from structures of minor importance or spurious ridges can be limited to the building locale, while the common features can be detected integrally. Experiments on various data show that the proposed method can obviously improve the topology quality and produce more precise results. Compared with the RTG-based method, two topology quality indices increase from 80.9% and 79.8% to 89.4% and 88.2% in the test area. The integral model quality indices at the pixel level and the plane level also achieve the precision of 90.3% and 84.7%, accordingly.

Under the influence of various forces, the conflict between Russia and Ukraine is violent and changeable. The obtaining of battlefield data by conventional means is difficult but necessary in order to ensure security, reliability, and comprehensiveness. The use of remote sensing technology can make up for the deficiencies of conventional methods. By using night-time light data, the total number of night-time lights in the built-up areas of Ukrainian cities within 36 days of the outbreak of the Russian–Ukrainian conflict is compiled in this paper. Furthermore, the dynamic changes in night-time light at the national, regional, and urban scales are analyzed by using the night-time light ratio index and the dynamic degree model combined with the time-series night-time light data. The results show that (1) after the outbreak of the war, more than 60% of the night-time lights in Ukrainian cities were lost. In terms of the night-time light recovery speed, the night-time lights in the pro-Russian areas recovered significantly faster, followed by Russian-controlled areas, and the recovery speed in areas of conflict was the lowest. (2) Decision-making by belligerents affects non-combatant activities and thus corresponds to light at night. The loss of night-time light will be reduced if military operations are reduced and mitigated if humanitarian operations are increased. (3) The changes in night-time light reflect the changes in the conflict situation well. When the conflict between Russia and Ukraine intensifies, the overall change of night-time light shows a downward trend. In this context, night-time light data can be used as an effective source to deduce and predict battlefield situations.

Road cracks represent a significant challenge that impacts the long-term performance and safety of transportation infrastructure. Early identification of these cracks is crucial for effective road maintenance management. However, traditional crack recognition methods that rely on visible light images often experience substantial performance degradation in weak-light environments, such as at night or within tunnels. This degradation is characterized by blurred or deficient image textures, indistinct target edges, and reduced detection accuracy, which hinders the ability to achieve reliable all-weather target detection. To address these challenges, this study introduces a dual-modal crack detection method named YOLOv11-DCFNet. This method is based on an enhanced YOLOv11 architecture and incorporates a Cross-Modality Fusion Transformer (CFT) module. It establishes a dual-branch feature extraction structure that utilizes both infrared and visible light within the original YOLOv11 framework, effectively leveraging the high contrast capabilities of thermal infrared images to detect cracks under weak- or no-light conditions. The experimental results demonstrate that the proposed YOLOv11-DCFNet method significantly outperforms the single-modal model (YOLOv11-RGB) in both weak-light and no-light scenarios. Under weak-light conditions, the fusion model effectively utilizes the weak texture features of RGB images alongside the thermal radiation information from infrared (IR) images. This leads to an improvement in Precision from 83.8% to 95.3%, Recall from 81.5% to 90.5%, mAP@0.5 from 84.9% to 92.9%, and mAP@0.5:0.95 from 41.7% to 56.3%, thereby enhancing both detection accuracy and quality. In no-light conditions, the RGB single modality performs poorly due to the absence of visible light information, with an mAP@0.5 of only 67.5%. However, by incorporating IR thermal radiation features, the fusion model enhances Precision, Recall, and mAP@0.5 to 95.3%, 90.5%, and 92.9%, respectively, maintaining high detection accuracy and stability even in extreme no-light environments. The results of this study indicate that YOLOv11-DCFNet exhibits strong robustness and generalization ability across various low illumination conditions, providing effective technical support for night-time road maintenance and crack monitoring systems.

Facing the challenge of the degradation of global water quality, it is urgent to realize the Sustainable Development Goal 6.3.2 (SDG 6.3.2), which focuses on improving global water quality. Currently, remote sensing technology is widely used for water quality monitoring. Existing water quality-monitoring studies have been conducted based on quantitative water quality inversion. It requires a high degree of the synchronization of the time and location of the collection of station monitoring data and remote sensing data (air–ground spatiotemporal synchronization), which can be resource intensive and time consuming. However, policymakers and the public are more interested in the quality of water (good or poor) than in the specific values of the water quality parameters, as evidenced by the emergence of SDG 6.3.2. In this study, we change the traditional idea of quantitative water quality research, focus on water quality qualitative research combined with the characteristics of water pollution, propose a remote sensing water quality sample enhancement method under the condition of “air–ground spatiotemporal asynchrony”, and construct a remote sensing water quality sample library. On the basis of this sample library, a random forest water quality classification model was constructed to classify water quality qualitatively. We obtained the distribution of good water bodies in Deqing County, China, for example, from 2013 to 2022. The results show that the model has high accuracy (Kappa = 0.6004, OA = 0.8387), and we found that the water quality in Deqing County improved in the order of “major rivers, lakes, and tributaries” during the period from 2013 to 2015. This also verifies the feasibility of using this sample enhancement method to conduct qualitative research on water quality. Based on this water quality classification model, a set of spatial-type evaluation processes of SDG 6.3.2 based on image elements was designed. The evaluation results show that the water quality situation in Deqing County can be divided into two stages: there is a trend of substantial improvement from 2013 (evaluated value of SDG 6.3.2 = 63.25) to 2015 (evaluated value of SDG 6.3.2 = 83.16); and it has remained stable and fluctuating after reaching the good environmental water quality since 2015. This study proposes a simple method for rapidly evaluating SDG 6.3.2 via utilizing easily accessible Landsat 8 and water quality-monitoring data to classify water quality. The method can directly obtain water quality category information without the need for additional sampling, thus saving costs. It is a very simple process that is easy to implement, while also providing a high level of accuracy. This significantly reduces the barriers to evaluating SDG 6.3.2, supports the realization of the sustainable management of water resources globally, and is highly generalizable.