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Geological remote sensing interpretation plays a pivotal role in the field of regional geological mapping, encompassing the analysis of rock, soil, and water features. However, these geological elements can be obscured by the surrounding geographical environment and can undergo modifications caused by geological activities. The former hinders the effectiveness of satellite remote sensing data, resulting in the invisibility of element features, while the latter leads to the complex distribution of element features and significant spatial variations of geological elements. Consequently, existing deep learning-based models for interpreting geological elements often exhibit limited accuracy. To address these issues, this study proposes the contextually enhanced multiscale feature fusion network for the efficient interpretation of geological elements. First, the context enhancement module is employed to extract abundant feature information and reinforce contextual features, aiming to capture essential features and strengthen their interconnections. Second, the multiscale feature fusion module incorporates the SimAM attention mechanism to adaptively learn features from different channels, emphasizing the feature information that contributes to interpretation results and maximizing the comprehensive and crucial feature information for each element. Extensive experiments demonstrate the superior performance of both the context enhancement module and the multiscale feature fusion module compared to several representative deep learning networks in terms of overall interpretation accuracy on two datasets. The model demonstrated improvements in oPA and mIoU of 2.4% and 2.8%, respectively, on the Landsat 8 dataset, and 3.5% and 3.2%, respectively, on the Sentinel-2 dataset.

Summary: As heavy metal industrial wastewater increases in volume and complexity, we need more efficient, cheaper, and renewable technologies to curb its environmental impact. Compared to advection electrosorption, through-flow electrosorption is a hotspot technique that makes more efficient use of the adsorption capacity of activated carbon fiber mats. A cascade flow-through electrosorption assembly based on activated carbon fiber was used to obtain the best adsorption of Zn2+ in water at a voltage of 2 V, pH value of 8, plate spacing of 3 mm, and temperature of 15°C. The process is more closely fitted to the secondary adsorption kinetic equation and the Langmuir equation. The adsorption capacity of the module decreases at a progressively slower rate with the number of cycles and will eventually retain 75% of its peak value with significant regenerability. The study of this module can provide technical support for treating heavy metal wastewater.

Global warming is melting glaciers. Changes in mountain glaciers have a tremendous impact on human life. Regular identification and extraction of glaciers from satellite images are necessary. However, when studying glaciers, materials surrounding the glacier have high spectral similarity to glaciers and are easily misclassified in the identification process. Therefore, in this study of glacier extraction, we used an improved U-Net model (a channel-attention U-Net) to map glaciers. The model was trained on Landsat 8 Operational Land Imager (OLI) data and a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), and was tested on glaciers in the Pamir Plateau. The results show that the channel-attention U-Net identifies glaciers with relatively high accuracy compared to U-Net and GlacierNet. The obtained results were fine-tuned by the conditional random field model, effectively reducing background misidentification.

Building extraction refers to the automatic identification and separation of buildings from the background in remote sensing images. It plays a significant role in urban planning, land management, and disaster monitoring. Deep-learning methods have shown advantages in building extraction, but they still face challenges such as variations in building types, object occlusions, and complex backgrounds. To address these issues, SDSNet, a deep convolutional network that incorporates global multi-scale feature extraction and cross-level feature fusion, is proposed. SDSNet consists of three modules: semantic information extraction (SIE), multi-level merge (MLM), and semantic information fusion (SIF). The SIE module extracts contextual information and improves recognition of multi-scale buildings. The MLM module filters irrelevant details guided by high-level semantic information, aiding in the restoration of edge details for buildings. The SIF module combines filtered detail information with extracted semantic information for refined building extraction. A series of experiments conducted on two distinct public datasets for building extraction consistently demonstrate that SDSNet outperforms the state-of-the-art deep-learning models for building extraction tasks. On the WHU building dataset, the overall accuracy (OA) and intersection over union (IoU) achieved impressive scores of 98.86% and 90.17%, respectively. Meanwhile, on the Massachusetts dataset, SDSNet achieved OA and IoU scores of 94.05% and 71.6%, respectively. SDSNet exhibits a unique advantage in recovering fine details along building edges, enabling automated and intelligent building extraction. This capability effectively supports urban planning, resource management, and disaster monitoring.

Accurate bathymetric maps are essential to understand marine and coastal ecosystems. With the development of satellite and sensor technology, satellite-derived bathymetry (SDB) has been widely used to measure the depth of nearshore waters. Employment of physics-based methods requires a series of optical parameters of the water column and seafloor, which limits the application of these methods to shallow-water bathymetry. Due to convenience, low costs, and high efficiency, empirical methods based on in situ measurements and satellite imagery are increasingly used for nearshore bathymetry. These measurements are required to calibrate empirical models, so that reasonable accuracy can be achieved. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), equipped with the advanced topographic laser altimeter system, provides a novel opportunity for nearshore bathymetry. Using the new measurement strategy of photon counting, ICESat-2 can provide accurate bathymetric points from spaceborne observations, which can be used in place of in situ water depth data. In this study, ICESat-2 bathymetric points and multispectral images were used to train four typical models and produce bathymetric maps for Shanhu Island, Ganquan Island, and Lingyang Reef in the Xisha Islands of China. We evaluated the bathymetric results by comparing them with reference depth data from airborne light detection and ranging. All models had a satisfactory accuracy, as well as multimodel and multisource image consistency. With the ICESat-2 bathymetric points, SDB is no longer limited by in situ measurements. Hence, this approach could be extended to a larger scale to obtain nearshore bathymetric maps of coastal areas, surrounding islands, and reefs using free and open-access satellite data.

With the significant improvements in Earth observation (EO) technologies, remote sensing (RS) data exhibit the typical characteristics of Big Data. Propelled by the powerful feature extraction capabilities of intelligent algorithms, RS image interpretation has drawn remarkable attention and achieved progress. However, the semantic relationship and domain knowledge hidden in massive RS images have not been fully exploited. To the best of our knowledge, a comprehensive review of recent achievements regarding semantic graph-based methods for comprehension and interpretation of RS images is still lacking. Specifically, this article discusses the main challenges of RS image interpretation and presents a systematic survey of typical semantic graph-based methodologies for RS knowledge representation and understanding, including the Ontology Model, Geo-Information Tupu, and Semantic Knowledge Graph. Furthermore, we categorize and summarize how the existing technologies address different challenges in RS image interpretation based on semantic graph-based methods, which indicates that the semantic information about potential relationships and prior knowledge of variant RS targets are central to the solution. In addition, a case study of RS geological interpretation based on the semantic knowledge graph is demonstrated to show the promising capability of intelligent RS image interpretation. Finally, the future directions are discussed for further research.

The development of domestic high resolution remote sensing satellite technology provides a new means for the investigation of macroscopic, local and detailed characteristics of the Quaternary.Alluvial-diluvial fans, valley terraces, glacial deposits are widely distributed in the eastern section of China-Pakistan Economic Corridor. In order to study the applicability of domestic high-resolution data in the Quaternary geological survey of this region, the alluvial and flood fan in front of Uruat mountain along Gaizi River basin of China-Pakistan highway and three typical Quaternary strata of Gaizi River valley terrace and Kraguirake glacial moraine were selected as the research objects in this study.Using domestic GF-2 images as the main data sources, with the aid of digital elevation model data, the remote sensing interpretation marker was established, and the fine geological interpretation was carried out.The composition and variation were found out through field verification.The previous distribution range of different periods of glacial moraines in Kailaillak was revised.Based on the analysis of the terrace in Gaizi River valley, the study area experienced at least five stages of tectonic uplift since late Epipleistocene, and the terrace base was formed by at least two stages of glaciation.This study shows that GF-2 images can not only quickly identify the change characteristics of landforms and loose deposits at macro scale, but also it can get geological phenomena that cannot be observed by conventional methods.And then it can meet the requirements of large scale interpretation and cartography.Especially in the identification of micro-geomorphology and interpretation of Quaternary strata, it can improve the level of fine geological interpretation.Research results can provide basic geological data for understanding the development and evolution process of fluvial and glaciation in the Gaizi River basin.Besides, this study can provide a typical example for remote sensing investigation of Quaternary soil mass along the China-Pakistan highway.

Road extraction is crucial in urban planning, rescue operations, and military applications. Compared to traditional methods, using deep learning for road extraction from remote sensing images has demonstrated unique advantages. However, previous convolutional neural networks (CNN)-based road extraction methods have had limited receptivity and failed to effectively capture long-distance road features. On the other hand, transformer-based methods have good global information-capturing capabilities, but face challenges in extracting road edge information. Additionally, existing excellent road extraction methods lack validation for the Chinese region. To address these issues, this paper proposes a novel road extraction model called the global–local information fusion network (GLNet). In this model, the global information extraction (GIE) module effectively integrates global contextual relationships, the local information extraction (LIE) module accurately captures road edge information, and the information fusion (IF) module combines the output features from both global and local branches to generate the final extraction results. Further, a series of experiments on two different Chinese road datasets with geographic robustness demonstrate that our model outperforms the state-of-the-art deep learning models for road extraction tasks in China. On the CHN6-CUG dataset, the overall accuracy (OA) and intersection over union (IoU) reach 97.49% and 63.27%, respectively, while on the RDCME dataset, OA and IoU reach 98.73% and 84.97%, respectively. These research results hold significant implications for road traffic, humanitarian rescue, and environmental monitoring, particularly in the context of the Chinese region.

Antarctica is covered by huge snow and ice, and its geological structure is bounded by the Trans-Antarctic Mountains, and is generally divided into the East Antarctic Shield and the West Antarctic Active Zone. Digital elevation model (DEM) is one of the important basic data to study the Antarctic ice sheet change. Obtaining elevation change information through the comparison of multi-period DEM is an important means to analyze the changes in the thickness and material balance of the Antarctic ice sheet. However, the horizontal and vertical errors between different types of DEM affect the accuracy of the analysis results. In this study, the horizontal error first eliminated by registration between DEMs, then elevation difference and standard deviation are calculated between CryoSat DEM and other DEMs, finally the temporal and spatial variation characteristics are evaluated. The results show that in the plane, the horizontal offset between TanDEM_X DEM and CryoSat DEM is the smallest, while that between ICESat DEM and CryoSat DEM is the largest. In the vertical direction, within the gradient range of 0° ~ 1°, the mean elevation difference between CryoSat DEM and TanDEM_X DEM is between 3.5 and 5.5 m, and the standard deviation is less than 18.0 m. The mean elevation difference between CryoSat DEM and Bamber 1km DEM is between -2.5 and +1.0 m, and the standard deviation is less than 24.2 m. The mean elevation difference between CryoSat DEM and ICESat DEM is between -25.0 and -1.0 m, and the standard deviation is less than 47.2 m. The mean elevation difference between CryoSat DEM and RAMPv2 DEM is between 1.3 and 3.2 m, and the standard deviation is less than 45.6 m. It is found that the internal elevation of Antarctic ice sheet increases, but the elevation of southwest ice sheet and southeast ice sheet decreases, and the southwest pole decreases obviously, while the elevation of Antarctic edge area decreases obviously. This study provides an important reference for global change research and Antarctic material balance research.

At present, there are more than 190 craters have been confirmed on the earth, and most of the simple impact craters with a diameter of less than 1 km are formed by iron impactor.There is a large amount of iron meteorite material around the impact crater struck by the iron impactor.The spatial distribution characteristics of iron meteorite are of great significance for studying the impact process and mechanism of the crater.The enrichment of iron can also be used as important information to explore suspected impact craters on the Earth's surface.In order to obtain iron meteorite fragments around the impact crater, field investigations were mainly conducted manually in the early days, but this method was inefficient and required a lot of manpower and material resources.Based on the unique spectral characteristics of the meteorite, the spatial distribution characteristics of the meteorite material around the impact crater can be easily obtained by using the remote sensing alteration information extraction method.This paper selects Barringer Meteor Crater in Arizona, USA as the research object, considering of the spectral characteristics of iron meteorite, and using Band Math-PCA(principal components analysis).The extraction results fit well with the field survey of the distribution of iron meteorites by the predecessors.The iron meteorite aggregation area on the east, southeast and southwest sides of the impact crater was well reflected in the extraction results.The iron meteorite around the impact crater was well extracted, and the iron meteorite gathering areas on the east, southeast and southwest sides were well reflected on the extraction result map.It shows that it is feasible to use the principal component analysis-band ratio method to extract the iron meteorite information around the Barringer crater.The experimental results accurately obtained the spatial distribution information of iron meteorite around the impact crater.This paper provides a feasible plan for exploring the ring structure of the impact on the earth's surface, and provides an important method reference for the extraction of similar impact craters in the future.

The Badain Jaran Desert (BJD) and surrounding deserts are the main sources of sand and dust storms in Asia. However, for complex factors, the descriptions of the sand dune dynamics in the BJD and investigations on the contribution of the BJD to the formation of the Tengger Desert (TD) and the Ulan Buh Desert (UBD) are lacking. We evaluated the performance of the discrete Fourier transform method in achieving subpixel precision when measuring the displacements of sand dunes in the BJD and surrounding deserts. This method was applied to Landsat 5/8 and 7 scan-line-corrector (SLC)-off/8 image pairs, respectively. The results show that it is a robust method in desert conditions without ground control points. Nineteen scenes of Landsat 5/8 were tested for estimating the movements of the BJD and surrounding areas. We found that the sand dunes moved eastward during 2004–2016. However, the dunes’ movements showed different patterns in different parts of the desert. In the western BJD, the dunes moved at an average speed of 1–3 myr−1, while in the southern and middle parts of the BJD, the dunes’ speed was about 0.1–1 myr−1. The fastest displacements of dunes were located in the northeastern BJD, and the mean speed was about 12 myr−1. The sand fluxes in the two corridors between the BJD and the TD and UBD were estimated. The annual total amount of sand transported from the BJD to the TD through the main corridor was about 0.95 million tonnes, while that from the BJD to the UBD was about 2.24 million tonnes. The estimations of dune displacements and sand transport based on Landsat images in this study are important for understanding the dynamics of the BJD and surrounding areas.