Powered by advances of optical remote sensing sensors, the production of very high spatial resolution multispectral images provides great potential for achieving cost-efficient and high-accuracy forest inventory and analysis in an automated way. Lots of studies that aim at providing an inventory of the level of each individual tree have generated many methods for Individual Tree Crown Detection and Delineation (ITCD). This review covers ITCD methods for detecting and delineating individual tree crowns, and systematically reviews the past and present of ITCD-related researches applied to the optical remote sensing images. With the goal to provide a clear knowledge map of existing ITCD efforts, we conduct a comprehensive review of recent ITCD papers to build a meta-data analysis, including the algorithm, the study site, the tree species, the sensor type, the evaluation method, etc. We categorize the methods into three classes: (1) traditional image processing methods (such as local maximum filtering, image segmentation); (2) traditional machine learning methods (such as random forest, decision tree); and (3) deep learning-based methods. With deep learning-oriented approaches contributing to a majority, we further discuss deep learning-based methods like semantic segmentation and object detection methods. In addition, we discuss four ITCD-related issues to further comprehend the ITCD domain using optical remote sensing data, such as comparisons between multi-sensor-based data and optical data in the ITCD domain, comparisons among different algorithms and different ITCD tasks, etc. Finally, this review proposes some ITCD-related applications and a few exciting prospects and potential hot topics in future ITCD research.

In high-resolution remote sensing images (RSIs), complex composite object detection (e.g., coal-fired power plant detection and harbor detection) is challenging due to multiple discrete parts with variable layouts leading to complex weak inter-relationship and blurred boundaries, instead of a clearly defined single object. To address this issue, this article proposes an end-to-end framework, i.e., relational part-aware network (REPAN), to explore the semantic correlation and extract discriminative features among multiple parts. Specifically, we first design a part region proposal network (P-RPN) to locate discriminative yet subtle regions. With butterfly units (BFUs) embedded, feature-scale confusion problems stemming from aliasing effects can be largely alleviated. Second, a feature relation Transformer (FRT) plumbs the depths of the spatial relationships by part-and-global joint learning, exploring correlations between various parts to enhance significant part representation. Finally, a contextual detector (CD) classifies and detects parts and the whole composite object through multirelation-aware features, where part information guides to locate the whole object. We collect three remote sensing object detection datasets with four categories to evaluate our method. Consistently surpassing the performance of state-of-the-art methods, the results of extensive experiments underscore the effectiveness and superiority of our proposed method.

Wetlands constitute critical ecosystems that support both biodiversity and human well-being; however, they have experienced a significant decline since the 20th century. Since the 1970s, researchers have employed remote sensing technologies for wetland classification and mapping to elucidate the extent and variation of wetlands. Although several review articles have summarized developments in this field, a comprehensive and in-depth synthesis remains lacking. This review article aims to address the following key questions: 1) What is the scientific importance of wetlands? 2) What are the major data sources and methods used in wetland classification and mapping? 3) What are the driving factors behind wetland changes? 4) What are the prevailing research paradigms and their limitations? 5) What challenges and opportunities lie ahead in the context of technological innovation and global environmental change? To answer these questions, we conducted a meta-analysis of over 1,200 peer-reviewed publications, analyzing trends in wetland types, mapping methods, sensor usage, and geographic focus. We review and synthesize current knowledge of wetland features, data availability, and methodological advances. Furthermore, we examine typical wetland mapping products and explore intrinsic driving factors across various spatial and temporal scales. Finally, we discuss major research limitations and propose future directions to improve wetland classification and monitoring in the era of global change. This review consolidates current understanding in wetland remote sensing and provides actionable recommendations to promote scientific progress in wetland studies.

Vast amounts of remote sensing (RS) data provide Earth observations across multiple dimensions, encompassing critical spatial, temporal, and spectral information which is essential for addressing global-scale challenges such as land use monitoring, disaster prevention, and environmental change mitigation. Despite various pre-training methods tailored to the characteristics of RS data, a key limitation persists: the inability to effectively integrate spatial, temporal, and spectral information within a single unified model. To unlock the potential of RS data, we construct a Spatial-Temporal-Spectral Structured Dataset (STSSD) characterized by the incorporation of multiple RS sources, diverse coverage, unified locations within image sets, and heterogeneity within images. Building upon this structured dataset, we propose an Anchor-Aware Masked AutoEncoder method (A²-MAE), leveraging intrinsic complementary information from the different kinds of images (featuring different resolutions, spectral compositions, and acquisition times) and geo-information to reconstruct the masked patches during the pre-training phase. Moreover, A²-MAE integrates an anchor-aware masking strategy and a geographic encoding module to comprehensively exploit the properties of RS images. Specifically, the proposed anchor-aware masking strategy dynamically adapts the masking process based on the meta-information of a pre selected anchor image, thereby facilitating the training on images captured by diverse types of RS sources within one model. Furthermore, we propose a geographic encoding method to leverage accurate spatial patterns, enhancing the model generalization capabilities for downstream applications that are generally location-related. Extensive experiments demonstrate our method achieves comprehensive improvements across various downstream tasks compared with existing RS pre-training methods, including image classification, semantic segmentation, and change detection tasks. The dataset and pre-training model is available at https://github.com/ZhaoYi1222/AAMAE.

The accurate detection of coal-fired power plants (CFPPs) is meaningful for environmental protection, while challenging. The CFPP is a complex combination of multiple components with varying layouts, unlike clearly defined single objects, such as vehicles. CFPPs are typically located in industrial districts with similar backgrounds, further complicating the detection task. To address this issue, we propose a MUltistage Recursive Enhanced Detection Network (MUREN) for accurate and efficient CFPP detection. The effectiveness of MUREN lies in the following: First, we design a symmetrically enhanced module, including a spatial-enhanced subnetwork (SEN) and a channel-enhanced subnetwork (CEN). SEN learns the spatial relationships to obtain spatial context information. CEN provides adaptive channel recalibration, restraining noise disturbance and highlighting CFPP features. Second, we use a recursive construction set on top of feature pyramid networks to receive features more than once, strengthening feature learning for relatively small CFPPs. We conduct comparative and ablation experiments in two datasets and apply MUREN to the Pearl River Delta region in Guangdong province for CFPP detection. The comparative experiment results show that MUREN improves the mAP by 5.98% compared with the baseline method and outperforms by 4.57–21.38% the existing cutting-edge detection methods, which indicates the promising potential of MUREN in large-scale CFPP detection scenarios.