A5100693229- Cryospheric studies and observations
- Remote-Sensing Image Classification
- Hydrology and Watershed Management Studies
- Climate change and permafrost
- Remote Sensing and Land Use
- Remote Sensing in Agriculture
- Advanced Image and Video Retrieval Techniques
- Land Use and Ecosystem Services
- Flood Risk Assessment and Management
- Advanced Fiber Optic Sensors
- Arctic and Antarctic ice dynamics
- Luminescence Properties of Advanced Materials
- Plant Water Relations and Carbon Dynamics
- Nanoplatforms for cancer theranostics
- Climate variability and models
- Advanced Image Fusion Techniques
- Urban Heat Island Mitigation
- Automated Road and Building Extraction
- Inorganic Fluorides and Related Compounds
- Perovskite Materials and Applications
- Hydrology and Drought Analysis
- Photonic and Optical Devices
- Advanced Photocatalysis Techniques
- Climate change impacts on agriculture
- Metal-Organic Frameworks: Synthesis and Applications
Nanjing University
2016-2025
Xinjiang Medical University
2015-2025
Northwest Normal University
2025
Ministry of Natural Resources
2015-2024
China Agricultural University
2015-2024
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application
2016-2024
Shanghai Jiao Tong University
2021-2024
Southwest University
2020-2024
Renji Hospital
2024
Anhui University
2024
Deep learning (DL) algorithms have seen a massive rise in popularity for remote-sensing image analysis over the past few years. In this study, major DL concepts pertinent to are introduced, and more than 200 publications field, most of which were published during last two years, reviewed analyzed. Initially, meta-analysis was conducted analyze status remote sensing studies terms study targets, model(s) used, spatial resolution(s), type area, level classification accuracy achieved....
Abstract Excessive emissions of greenhouse gases — which carbon dioxide is the most significant component, are regarded as primary reason for increased concentration atmospheric and global warming. Terrestrial vegetation sequesters 112–169 PgC (1PgC = 10 15 g carbon) each year, plays a vital role in recycling. Vegetation sequestration varies under different land management practices. Here we propose an integrated method to assess how much more can be sequestered by if optimal practices get...
A critical obstacle to achieve semantic segmentation of remote sensing images by the deep convolutional neural network is requirement huge pixel-level labels. Taking building extraction as an example, this study focuses on how effectively apply weakly supervised (WSSS) high-resolution (HR) with image-level labels, which a prominent solution for labeling challenge. The widely used two-step WSSS framework adopted, in pseudo-masks are first produced from labels and followed trained...
Self-supervised learning (SSL) has gained widespread attention in the remote sensing (RS) and earth observation (EO) communities owing to its ability learn task-agnostic representations without human-annotated labels. Nevertheless, most existing RS SSL methods are limited either global semantic separable or local spatial perceptible representations. We argue that this strategy is suboptimal realm of RS, since required for different downstream tasks often varied complex. In study, we proposed...
Change detection is a critical task in earth observation applications. Recently, deep-learning-based methods have shown promising performance and are quickly adopted change detection. However, the widely used multiple encoders single decoder (MESD) as well dual-encoder–decoder (DED) architectures still struggle to effectively handle well. The former has problems of bitemporal feature interference feature-level fusion, while latter inapplicable intraclass (ICCD) multiview building (MVBCD). To...
Promoting appropriate water-saving irrigation is an important pathway to alleviate groundwater overexploitation in irrigated agriculture. Whether alternative method feasible relies on quantitative assessments of its impacts crop yield and hydrological cycle, especially the overdraft area that faces a dilemma between water scarcity food security. Using groundwater-module-improved SWAT (Soil & Water Assessment Tool) model, five GCMs (General Circulation Models) projections CMIP6 (Coupled Model...
China has experienced a rapid urban expansion over the past three decades because of its accelerated economic growth. In this study, we detected and analyzed during period using multi-temporal Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) nighttime light data multi-source Normalized Difference Vegetation Index (NDVI) data. First, an intercalibration was performed to improve continuity comparability from 1992 2010. The NDVI were then subjected local support...
Tuberculosis is a major global public health problem, which also affects economic and social development. China has the second largest burden of tuberculosis in world. The morbidity Xinjiang much higher than national situation; therefore, there an urgent need for monitoring predicting so as to make control more effective. Recently, Box-Jenkins approach, specifically autoregressive integrated moving average (ARIMA) model, typically applied predict infectious diseases; it can take into account...
This paper presents a cosegmentation-based method for building change detection from multitemporal high-resolution (HR) remotely sensed images, providing new solution to object-based (OBCD). First, the magnitude of difference image is calculated represent feature. Next, cosegmentation performed via graph-based energy minimization by combining feature with features at each phase, directly resulting in foreground as changed objects and background unchanged area. Finally, spatial correspondence...
Abstract Controlling crystal size and shape of zeolitic materials is an effective way to promote their mass transport catalytic properties. Herein, we report a single step, Na + ‐ porogen‐ free crystallization MFI hierarchical architecture made up aligned nanocrystals with reduced b ‐axis thickness (5–23 nm) adjustable Si/Al ratios between 35 120, employing the commonly used tetrapropylammonium hydroxide (TPAOH) tetrabutylammonium (TBAOH) as structure‐directing agents (SDAs). Homogeneous...
Learning effective visual representations without human supervision is a critical problem for the task of semantic segmentation remote sensing images (RSIs), where pixel-level annotations are difficult to obtain. Self-supervised learning (SSL), which learns useful by creating artificial supervised problems, has recently emerged as an method learn from unlabelled data. Current SSL methods generally trained on ImageNet through image-level prediction tasks. We argue that this suboptimal...
Semantic segmentation of remote sensing images is effective for large-scale land cover mapping, which heavily relies on a large amount training data with laborious pixel-level labeling. Weakly supervised semantic (WSSS) based image-level labels has attracted intensive attention due to its easy availability. However, existing WSSS methods mainly focus binary segmentation, are difficult apply multiclass scenarios. This study proposes comprehensive framework images, consisting appropriate label...