The South China Karst, a United Nations Educational, Scientific and Cultural Organization (UNESCO) natural heritage site, is one of the world’s most spectacular examples of humid tropical to subtropical karst landscapes. The Libo cone karst in the southern Guizhou Province is considered as the world reference site for these types of karst, forming a distinctive and beautiful landscape. Geomorphic information and spatial distribution of cone karst is essential for conservation and management for Libo heritage site. In this study, a deep learning (DL) method based on DeepLab V3+ network was proposed to document the cone karst landscape in Libo by multi-source data, including optical remote sensing images and digital elevation model (DEM) data. The training samples were generated by using Landsat remote sensing images and their combination with satellite derived DEM data. Each group of training dataset contains 898 samples. The input module of DeepLab V3+ network was improved to accept four-channel input data, i.e., combination of Landsat RGB images and DEM data. Our results suggest that the mean intersection over union (MIoU) using the four-channel data as training samples by a new DL-based pixel-level image segmentation approach is the highest, which can reach 95.5%. The proposed method can accomplish automatic extraction of cone karst landscape by self-learning of deep neural network, and therefore it can also provide a powerful and automatic tool for documenting other type of geological landscapes worldwide.

Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imaging system onboard NASA’s (National Aeronautics and Space Administration’s) Terra satellite is capable of measuring multispectral reflectance of the earth’s surface targets in visible and infrared (VNIR) to shortwave infrared (SWIR) (until 2006) as well as multispectral thermal infrared (TIR) regions. ASTER VNIR stereo imaging technique can provide high-resolution digital elevation models (DEMs) data. The DEMs data, three-dimensional (3D) perspective, and ratio images produced from the ASTER multispectral data are employed to analyze the geomorphologic and lithologic features of Wudalianchi volcanoes in the northeastern China. Our results indicate that the 14 major conical volcanic craters of Wudalianchi volcanoes are arranged as three sub-parallel zones, extending in a NE (Northeast) direction, which is similar to the direction of regional fault system based on the ASTER DEMs data. Among the 14 volcanic craters in Wudalianchi, the Laoheishan, and Huoshaoshan lavas flows, after the historic eruptions, pouring down from the crater, partially blocked the Baihe River, which forms the Five Large Connected Pools, known as the Wudalianchi Lake. Lithologic mapping shows that ASTER multispectral ratio imagery, particularly, the Lava Flow Index (LFI) (LFI = B10/B12) imagery, can clearly distinguish different lava flow units, and at least four stages of volcanic eruptions are revealed in the Wudalianchi Quaternary volcano cluster. Thus, ASTER multispectral TIR data can be used to determine relative dating of Quaternary volcanoes in the semi-arid region. Moreover, ASTER 3D perspective image can present an excellent view for tracking the flow directions of different lavas of Wudalianchi Holocene volcanoes.

In the ship detection of the remote sensing images, cloud occlusion could blur the boundaries between ships and backgrounds, making it more challenging to distinguish them. Cloud occlusion can also result in partial or complete occlusion of target, making it difficult for models to detect ships in their entirety. Therefore, the use of cloud removal techniques is essential to enhance the accuracy and robustness of target detection. However, existing cloud removal processes are applied to entire images, providing limited improvements for specific object detection. In this letter, A Perlin Noise Based Thin Cloud Removal (PNBT-CR) Network is proposed for ship detection. The proposed algorithm introduces a Perlin noise mist mix module, which can improve the cloud removal effect of the network effectively. And it designed a Target-Oriented Structural Similarity (TOSS) loss function that enhances the network’s ability to boost the confidence of detected ships in the results. Experimental results demonstrate the efficacy of this approach in restoring texture details in ships and enhancing the accuracy of ship detection in remote sensing imagery. Moreover, the images processed using our method can have 89.86% SSIM compared to the original images, and when used for ship detection, there can be a maximum improvement of 11.7% F1-score.

Deep Neural Network (DNN)-based point cloud semantic segmentation has presented significant breakthrough using large-scale labeled aerial laser point cloud datasets. However, annotating such large-scaled point clouds is time-consuming. Self-Supervised Learning (SSL) is a promising approach to this problem by pre-training a DNN model utilizing unlabeled samples followed by a fine-tuned downstream task involving very limited labels. The traditional contrastive learning for point clouds selects the hardest negative samples by solely relying on the distance between the embedded features derived from the learning process, potentially evolving some negative samples from the same classes to reduce the contrastive learning effectiveness. This work proposes a hard-negative sample-aware self-supervised contrastive learning algorithm to pre-train the model for semantic segmentation. We designed a k-means clustering-based Absolute Positive And Negative samples (AbsPAN) strategy to filter the possible false-negative samples. Experiments on two typical ALS benchmark datasets demonstrate that the proposed method is more appealing than supervised training schemes without pre-training. Especially when the labels are severely inadequate (10% of the ISPRS training set), the results obtained by the proposed HAVANA method still exceed 94% of the supervised paradigm performance with full training set.

Karst rocky desertification (KRD) is a significant issue that affects the ecological and economic sustainability of southwest China. Obtaining the accurate distribution of different levels of KRD can provide decision-making support for the effective management of KRD. The Sustainable Development Goals Science Satellite 1 (SDGSAT-1) is the world’s first scientific satellite serving the 2030 Agenda for Sustainable Development of the United Nations, and is dedicated to developing high-resolution, multi-scale, global public datasets to support policy and decision-making support systems for sustainable development. SDGSAT-1 multispectral data provide detailed ground information with a spatial resolution of 10 m and a rich spectral resolution. In this study, we combined the red-modified carbonate rock index (RCRI, an index that characterizes the degree of carbonate rock exposure) and the normalized difference red edge index (NDRE, an index that characterizes the degree of vegetation coverage) to propose a novel feature space method based on SDGSAT-1 multispectral data to classify the different levels of KRD in the Jinsha County of Guizhou Province, a representative region with significant KRD in southwest China. This method effectively identified different levels of KRD with an overall classification accuracy of 87%. This was 20% higher than that of the grading index method, indicating that SDGSAT-1 multispectral data have promising potential for KRD classification. In this study, we offer a new insight into the classification of KRD and a greater quantity of remote-sensing data to monitor KRD over a wider area and for a longer period of time, contributing to the economic development and environmental protection of KRD areas.

Reliable sea ice extent (SIE) information is essential for Arctic navigation, climate research, and resource exploration. Synthetic Aperture Radar (SAR), with its all-weather, high-resolution capabilities, is well suited for SIE extraction. This study evaluates a pan-Arctic SIE product automatically generated from over 85,000 Sentinel-1 SAR images acquired between 2020 and 2023 using an integrated stacking U-Net framework. To validate its performance, all the SIE products are converted to sea ice concentration (SIC) and compared against the 3.125 km resolution Advanced Microwave Scanning Radiometer-2 (AMSR2) SIC products. The S1-derived SIC shows strong agreement with AMSR2 SIC, yielding a Pearson correlation of 0.99 and annual mean absolute differences between 5.93% and 7.85%. Case analyses demonstrate that the S1 products effectively capture small-scale ice features, such as floes, which are often missed by AMSR2. Furthermore, we introduce an Integrated Index to quantify the relative contribution of each sub-model within the integrated stacking U-Net framework. The analysis indicates that three sub-models provide the primary contribution to the ensemble, offering insights into improving integration efficiency and guiding the design of more scientifically grounded ensemble strategies.