Surface water is the most important resource and environmental factor in maintaining human survival and ecosystem stability; therefore, timely accurate information on dynamic surface water is urgently needed. However, the existing water datasets fall short of the current needs of the various organizations and disciplines due to the limitations of optical sensors in dynamic water mapping. The advancement of the cloud-based Google Earth Engine (GEE) platform and free-sharing Sentinel-1 imagery makes it possible to map the dynamics of a surface water body with high spatial-temporal resolution on a large scale. This study first establishes a water extraction method oriented towards Sentinel-1 Synthetic Aperture Radar (SAR) data based on the statistics of a large number of samples of land-cover types. An unprecedented high spatial-temporal water body dataset in China (HSWDC) with monthly temporal and 10-m spatial resolution using the Sentinel-1 data from 2016 to 2018 is developed in this study. The HSWDC is validated by 14,070 random samples across China. A high classification accuracy (overall accuracy = 0.93, kappa coefficient = 0.86) is achieved. The HSWDC is highly consistent with the Global Surface Water Explorer dataset and water levels from satellite altimetry. In addition to the good performance of detecting frozen water and small water bodies, the HSWDC can also classify various water cover/uses, which are obtained from its high spatial-temporal resolution. The HSWDC dataset can provide more detailed information on surface water bodies in China and has good application potential for developing high-resolution wetland maps.

High-quality road network information plays a vital role in regional economic development, disaster emergency management and land planning. To date, studies have primarily focused on sampling flat urban roads, while fewer have paid attention to road extraction in mountainous regions. Compared with road extraction in flat regions, road extraction in mountainous regions suffers more interference, due to shadows caused by mountains and road-like terrain. Furthermore, there are more practical problems involved when researching an entire region rather than at the sample level. To address the difficulties outlined regarding mountain road extraction, this paper takes Jiuzhaigou county in China as an example and studies road extraction in practical applications. Based on deep learning methods, we used a multistage optimization method to improve the extraction effect. First, we used the contrast limited adaptive histogram equalization (CLAHE) algorithm to attenuate the influence of mountain shadows and improve the quality of the image. Then the road was extracted by the improved DSDNet network. Finally, the terrain constraint method is used to reduce the false detection problem caused by the terrain factor, and after that the final road extraction result is obtained. To evaluate the effect of road extraction comprehensively, we used multiple data sources (i.e., points, raster and OpenStreetMap data) in different evaluation schemes to verify the accuracy of the road extraction results. The accuracy of our method for the three schemes was 0.8631, 0.8558 and 0.8801, which is higher than other methods have obtained. The results show that our method can effectively solve the interference of shadow and terrain encountered in road extraction over mountainous regions, significantly improving the effect of road extraction.

Image segmentation plays a significant role in remote sensing image processing. Among numerous segmentation algorithms, the region-merging segmentation algorithm is widely used due to its well-organized structure and outstanding results. Many merging criteria (MC) were designed to improve the accuracy of region-merging segmentation, but each MC has its own shortcomings, which can cause segmentation errors. Segmentation accuracy can be improved by referring to the segmentation results. To achieve this, an approach for detecting and correcting region-merging image segmentation errors is proposed, and then an iterative optimization model is established. The main contributions of this paper are as follows: (1) The conflict types of matching segment pairs are divided into scale-expression conflict (SEC) and region-ownership conflict (ROC), and ROC is more suitable for optimization. (2) An equal-scale local evaluation method was designed to quantify the optimization potential of ROC. (3) A regional anchoring strategy is proposed to preserve the results of the previous iteration optimization. Three QuickBird satellite images of different land-cover types were used for validating the proposed approach. Both unsupervised and supervised evaluation results prove that the proposed approach can effectively improve segmentation accuracy. All explicit and implicit optimization modes are concluded, which further illustrate the stability of the proposed approach.

The Great Wildebeest Migrationin the Serengeti-Mara ecosystem is a globally iconic wildlife phenomenon that supports the health and biodiversity of the region by supporting predator populations, regulating herbivore densities, and driving nutrient cycling. This study presents the first AI-powered satellite survey, using two deep learning-based models (U-Net and YOLOv8) to detect and count wildebeest over more than 4,000 km² across two consecutive years in August 2022 and 2023 with F1 scores reaching 0.830 (Precision: 0.832, Recall: 0.838). The satellite-based results show fewer than 600,000 individuals—approximately half the widely cited estimate of 1.3 million wildebeest, which has remained largely unchanged since the 1970s. While some variation may arise from differences in spatial and temporal coverage between survey methods, the satellite approach employs rigorously validated AI models with demonstrated accuracy. Rather than undermining previous methods, this discrepancy underscores the importance of using independent and complementary monitoring tools to refine population estimates and improve our understanding of wildebeest movement dynamics.

Detecting wildebeest from very-high-resolution (VHR) satellite imagery enables large-area population monitoring in a single acquisition, avoiding aircraft-induced disturbance and reducing sampling bias caused by transect-based surveys. However, wildebeest appear as extremely small objects in satellite images, and direct application of classical object detectors (e.g., YOLO-style detectors) often yields poor performance. In particular, high-density aggregation areas suffer from severe missed detections due to scale mismatch and limitations in post-processing for densely packed small objects.To address these challenges, we develop a targeted detection solution that integrates (1) an adaptive sliding-window strategy to better capture local context under varying density conditions, (2) resolution–detector adaptation to mitigate scale mismatch between object size and detector design, and (3) improved post-processing modules, including an enhanced non-maximum suppression (NMS) tailored for dense small-object scenarios. We evaluate the proposed framework using WorldView-2 and WorldView-3 imagery over the Serengeti acquired in 2022 and 2023. The overall F1-score improves from 0.727 to 0.770 in 2022 and from 0.682 to 0.756 in 2023. Notably, in high-density areas in 2022, the F1-score increases from 0.330 to 0.821, demonstrating that our approach effectively reduces missed detections in dense small-object scenarios that commonly lead to substantial omissions in traditional pipelines.Beyond wildebeest monitoring, our results highlight a generalizable pathway for adapting classical detectors to dense small-object detection in VHR satellite imagery, where objects are tiny and crowded.