In China, landslides are abundant, widespread, and regular, destroying villages and agriculture and sometimes posing a threat to people’s lives. The question of how to rapidly detect and attain landslide data is a significant topic of research, yet traditional measurement using medium-resolution remote sensing data is problematic. Object-oriented categorization is utilized in this research to extract landside data from high-resolution GF-1 and Sentinel-2 data. Data preprocessing begins with orthophoto correction, image matching, and data fusion, followed by band enhancement, which comprises band synthesis, principal component analysis, and filtering, and finally landside extraction using an object-oriented technique. The impact of geology, lithology, rainfall, and human activities on the occurrence of landslides in the study area is explored utilizing DEM data, visualization tools, remote sensing interpretation map, and other associated data. The studies are conducted in Shuicheng County, Guizhou Province, China, with a segmentation scale of 25 pixels and 14 classification feature parameters. Following that, the landslide mass is extracted and categorization findings of nearby characteristics are acquired. Finally, the destructiveness of the landslide is determined by comparing the results of object-oriented classification before and after the landslide. With a Kappa coefficient of 0.76 and a landslide extraction accuracy of 79.8%, the overall classification accuracy is 87%. Combined with the geological structure, rock lithology, spatial location, landslide occurrence process, elevation of the study area, precipitation and the impact of human activities, the causes of the landslide are discussed and analyzed. The early warning of other unknown landslides can be obtained by analyzing the features of the aforementioned components.

The Jamuna is one of the world’s most heavily sediment-laden braided river. Despite a major host for heavy mineral resources, the sediments of the Jamuna river are sparsely studied. A mineralogical and SEM-EDS investigation has been carried out in the middle part (along Sirajganj and Jamalpur district of Bangladesh) of the Jamuna river to appraise the grain size parameter, texture, depositional environment, heavy mineral distribution, and elemental composition of the heavy minerals. The results show that sediment samples are very fine to fine sand, moderate to well sorted, near symmetrical to strongly fine skewed, and mesokurtic to very leptokurtic. Uniform to graded suspension mechanisms are the processes for sediment deposition. Heavy minerals accumulation ranges from 0.5% to 9.33%, and the assemblages are predominantly marked by epidote, amphibole, followed by opaque (magnetite and ilmenite), garnet, zircon, sillimanite, apatite, tourmaline, rutile, kyanite, staurolite, monazite, chlorite, and titanite. The percentage of amphibole, garnet, and zircon decreases downstream and epidote increases in the same direction. The calculated zircon-tourmaline-rutile index (ZTR) varied between 3 to 13%, average apatite-tourmaline index (ATi), garnet-zircon index (GZi), rutile-zircon index (RuZi) and staurolite-zircon index (SZi) values are 54.55%, 75%, 38.68%, and 30% respectively. The Brahmaputra-Jamuna river sediments are derived from mixed-source regions (e.g., Shillong Plateau, Indo-Burman Ranges, and the Himalayas). The abundance of subangular to angular, high relief heavy mineral grains with sharp edges on the surface, conchoidal fractures, deep troughs, and breakage blocks infer that the provenance is close to the depositional area. The presence of rounded to sub-rounded, smoother surface with adhering particles, arcuate steps, and low to medium relief indicate the long-distance transportation of heavy mineral grains through fluvial environment.

We aimed to improve the performance of ship detection methods in synthetic aperture radar (SAR) images by utilizing machine learning (ML) and artificial intelligence (AI) techniques. The maritime industry faces challenges in collecting precise data due to constantly changing sea conditions and weather, which can affect various maritime operations, such as maritime security, rescue missions, and real-time monitoring of water boundaries. To overcome these challenges, we present a survey of AI- and ML-based techniques for ship detection in SAR images that provide a more effective and reliable way to detect and classify ships in a variety of weather conditions, both onshore and offshore. We identified key features frequently used in the existing literature and applied the graph theory matrix approach (GTMA) to rank the available methods. This study’s findings can help users select a quick and efficient ship detection and classification method, improving the accuracy and efficiency of maritime operations. Moreover, the results of this study will contribute to advancing AI- and ML-based techniques for ship detection in SAR images, providing a valuable resource for the maritime industry.

In recent years, detecting and monitoring landslides have become increasingly critical for disaster management and mitigation efforts. Here, we propose a model for landslide detection utilizing a combination of the Swin Transformer architecture with multi-scale feature fusion lateral connection and local information aggregation modules. The Swin Transformer, known for its effectiveness in image understanding tasks, serves as the backbone of our detection system. By leveraging its hierarchical self-attention mechanism, the Swin Transformer can effectively capture both local and global contextual information from input images, facilitating accurate feature representation. To increase the performance of the Swin Transformer specifically for landslide detection, we introduce two additional modules: the multi-scale feature fusion lateral connection module and the local information aggregation module. The former module enables the integration of features across multiple scales, allowing the model to capture both fine-grained details and broader contextual information relevant to landslide characteristics. Meanwhile, the latter module focuses on aggregating local information within regions of interest, further refining the model's ability to discriminate between landslide and non-landslide areas. Through extensive test and evaluation of benchmark datasets, our proposed method demonstrates promising results in detecting landslides with high mIoU, F1 score, kappa, precision, and recall 84.2, 90.7%, 82.6%, 89.9%, and 91.9%, respectively. Moreover, its robustness to variations in terrain and environmental conditions suggests its potential for real-world applications in landslide monitoring and early warning systems. Overall, our study highlights the effectiveness of integrating advanced transformer architectures with tailored modules for addressing complex geospatial challenges like landslide detection.

The goal of the current study is to understand the elemental concentration, reserve assessment, mineralogical composition, and distribution of heavy minerals in the dune sands, foreshore, and backshore of Sonadia Island, Southeast Bangladesh. The study area yielded a number of drill hole composite sand samples that were used for density and magnetic separation of heavy minerals. These samples were then subjected to petrographic characterization, grain counting, mineralogical analyses using scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM–EDX), x-ray diffraction (XRD), and bulk sand geochemistry using X-ray fluorescence (XRF). The results of heavy liquid separation show that the total heavy mineral (THM) enrichments in foreshore sand (about 4.86%), backshore sand (about 9.03%), and dune sand (about 22.28%) are significantly higher. After characterizing the sand samples from Sonadia Island based on the abundance of each mineral species, the average content values relative to total economic heavy minerals (TEHM) were determined to be 38.14% ilmenite, 5.74% magnetite, 51.52% garnet, 1.01% zircon, 3.57% rutile, and 0.01% monazite. An SEM–EDX examination reveals that the elemental concentration of ZrO₂ in zircon is 66.60%, while the average TiO₂ content in rutile is ~ 95%, whereas TiO₂ is 45% and the total Fe₂O₃ is 45.70% in ilmenite. Major phases including amphibole (tremolite) 45.9%, muscovite 15.6%, quartz 12.6%, garnet (almandine) 10.9%, dolomite 7.9%, ilmenite 4.1%, and kaolinite 1.3% are also identified by the XRD pattern. There are an estimated 19,794 tons of garnet in the foreshore sand, 47,324 tons in the backshore sand, and 276,868 tons in the dune sand within the reserve, making it the most prevalent heavy mineral. Ilmenite is the second most common heavy mineral, with quantities detected in the foreshore (15,016 tons), backshore (30,487 tons), and dune sand (215,092 tons).