Deep-learning-based ship detection methods in Synthetic Aperture Radar (SAR) imagery are a current research hotspot. However, these methods rely on high-quality images as input, and in practical applications, SAR images are interfered with by speckle noise, leading to a decrease in image quality and thus affecting detection accuracy. To address this problem, we propose a unified framework for ship detection that incorporates a despeckling module into the object detection network. This integration is designed to enhance the detection performance, even with low-quality SAR images that are affected by speckle noise. Secondly, we propose a Multi-Scale Window Swin Transformer module. This module is adept at improving image quality by effectively capturing both global and local features of the SAR images. Additionally, recognizing the challenges associated with the scarcity of labeled data in practical scenarios, we employ an unlabeled distillation learning method to train our despeckling module. This technique avoids the need for extensive manual labeling and making efficient use of unlabeled data. We have tested the robustness of our method using public SAR datasets, including SSDD and HRSID, as well as a newly constructed dataset, the RSSDD. The results demonstrate that our method not only achieves a state-of-the-art performance but also excels in conditions with low signal-to-noise ratios.

Forests, are the largest carbon storage and carbon sinks on land and are closely related to global climate change. Using remote sensing images to monitor global deforestation has become the mainstream method, mainly because of their long time-series observation period and low cost. Although researchers have proposed several excellent deep-learning models to improve deforestation monitoring accuracy, few studies pay attention to designing an online deforestation detection system with deep-learning methods for non-expert users or researchers, due to the complicated development process or excessive cost investment. In this study, we aimed to develop an open-sourced and user-friendly online system for monitoring large-scale deforestation on high-resolution remote sensing imageries, named OpenForestMonitor. The OpenForestMonitor system was the first online deforestation detection system developed for highresolution remote sensing imageries with deep learning methods. To validate the performance of the OpenForestMonitor system, we selected four large-scale study areas in China as the study areas, and they are Liuyang City, Cangwu County, Sanya City, and Xianyang City. The final experiment results indicated that the F1-score of these study areas is 0.719, 0.854, 0.792, and 0.650, respectively. Furthermore, we also tested the performance of the OpenForestMonitor system on median-resolution Landsat remote sensing imageries and low-quality remote sensing images in complicated regions, and the final F1-score in Liuyang City and Cangwu County is 0.784 and 0.741, respectively. Compared with commercial software such as ArcGIS Pro 3.0, the Open- ForestMonitor also indicated an obvious efficiency advantage. The OpenForestMonitor system was open-sourced (https://github. com/wzp8023391/Deforestation-monitor-system), and it can also inspire researchers to develop other landcover, change detection, and object extraction online systems conveniently.

Accurate monitoring of the spatiotemporal dynamics of snow and ice is essential for under-standing and predicting the impacts of climate change on Arctic ecosystems and their feedback on global climate. Traditional optical and Synthetic Aperture Radar (SAR) remote sensing still have limitations in the long-time series observation of polar regions. Although several studies have demonstrated the potential of moonlight remote sensing for mapping polar snow/ice covers, systematic evaluation on applying moonlight remote sensing to monitoring spatiotemporal dynamics of polar snow/ice covers, especially during polar night periods is highly demanded. Here we present a systematic assessment in Svalbard, Norway and using data taken from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP) Day/Night Band (DNB) sensor to monitor the spatiotemporal dynamics of snow/ice covers during dark Arctic winters when no solar illumination available for months. We successfully revealed the spatiotemporal dynamics of snow/ice covers from 2012 to 2022 during polar night/winter periods, using the VIIRS/DNB time series data and the object-oriented Random Forests (RF) algorithm, achieving the average accuracy and kappa coefficient of 96.27% and 0.93, respectively. Our findings indicate that the polar snow/ice covers show seasonal and inter-seasonal dynamics, thus requiring more frequent observations. Our results confirm and realize the potential of moonlight remote sensing for continuous monitoring of snow/ice in the Arctic region and together with other types of remote sensing data, moonlight remote sensing will be a very useful tool for polar studies and climate change.

Offshore mariculture is critical for global food security and economic development. Advances in deep learning and data-driven approaches, enable the rapid and effective monitoring of offshore mariculture distribution and changes. However, detector performance depends heavily on training data quality. The lack of standardized classifications and public datasets for offshore mariculture facilities currently hampers effective monitoring. Here, we propose to categorize offshore mariculture facilities into six types: TCC, DWCC, FRC, LC, RC, and BC. Based on these categories, we introduce a benchmark dataset called OMAD-6. This dataset includes over 130,000 instances and more than 16,000 high-resolution remote sensing images. The images with a spatial resolution of 0.6 m were sourced from key regions in China, Chile, Norway, and Egypt, from the Google Earth platform. All instances in OMAD-6 were meticulously annotated manually with horizontal bounding boxes and polygons. Compared to existing remote sensing datasets, OMAD-6 has three notable characteristics: (1) it is comparable to large, published datasets in instances per category, image quantity, and sample coverage; (2) it exhibits high inter-class similarity; (3) it shows significant intra-class diversity in facility sizes and arrangements. Based on the OMAD-6 dataset, we evaluated eight state-of-the-art methods to establish baselines for future research. The experimental results demonstrate that the OMAD-6 dataset effectively represents various real-world scenarios, which have posed considerable challenges for current instance segmentation algorithms. Our evaluation confirms that the OMAD-6 dataset has the potential to improve offshore mariculture identification. Notably, the QueryInst and PointRend algorithms have distinguished themselves as top performers on the OMAD-6 dataset, robustly identifying offshore mariculture facilities even with complex environmental backgrounds. Its ongoing development and application will play a pivotal role in future offshore mariculture identification and management.

Offshore mariculture is a critical component of China’s aquaculture sector, but its rapid expansion presents significant challenges to sustainable marine resource management. This study utilizes high-resolution remote sensing data (2017–2023) and advanced ConvNeXt V2 algorithms to quantitatively analyze the spatiotemporal dynamics of offshore mariculture and explore its spatial distribution in relation to marine functional zoning policies. Through a detailed classification of six mariculture types, this study reveals significant spatial shifts, with China’s offshore mariculture transitioning from a model characterized by a “coastal, concentrated layout” to a new paradigm of “deep-sea and far-sea expansion, multi-point distribution”. Notably, the area of deep-sea and far-sea mariculture increased by 41.8% in regions with water depths of 50 m or more from 2018 to 2022. However, in 2022, the actual mariculture area accounted for only 0.608% of the designated functional zones, while 61.79% of mariculture activities occurred outside these planned zones, indicating a considerable spatial mismatch between mariculture practices and zoning plans. This study underscores the urgent need to optimize spatial planning and regulatory frameworks to balance economic growth with environmental sustainability, offering novel insights and actionable recommendations for the coordinated development of China’s marine economy.

Assessing urban resilience requires accurate data on building age as a temporal proxy associated with structural vulnerability, yet persistent cloud cover and rapid development constrain data availability in tropical and subtropical regions. We propose a computationally efficient framework that prioritizes annual data integrity over monthly granularity to map building construction years. By combining annual cloud-free Landsat NDVI (Normalized Difference Vegetation Index) composites with open-source building footprints, the framework utilizes a Temporal Template Matching (TTM) algorithm to detect the distinct “vegetation-to-built” transition signal. Evaluated across two dynamic and heavily cloud-contaminated metropolitan areas—Shenzhen, China, and Hanoi, Vietnam—this approach achieves a high producer’s accuracy; furthermore, in Shenzhen, where a monthly comparative analysis was conducted, it outperforms a noise-sensitive monthly LandTrendr-based baseline by a factor of nearly 1.8. Our findings demonstrate that under persistent cloud contamination, a coarser but consistent annual composite provides a more reliable signal than finer-grained alternatives. This scalable methodology generates critical building-age datasets, offering foundational structural intelligence for potential inputs into seismic risk modeling and resilient urban planning in rapid-growth and resource-constrained regions.