This study evaluated airborne laser scanning (ALS) as a large-scale tool for forest carbon quantification by comparing ALS-derived estimates with traditional field sampling across multiple forest strata. Above-ground biomass was estimated using two d...
The rapid assessment of building damage within a region after an earthquake is crucial for post-earthquake relief efforts. The current building damage assessment methods primarily employ remote sensing or structural equation modeling, which suffer fr...
Aerosol dry deposition is an important sink for particulate matter and a source of uncertainty in air quality modeling. Using the Weather Research and Forecasting model coupled with CUACE (WRF-CUACE), we quantified how three aerosol dry deposition sc...
Semantic segmentation of remote sensing images plays a crucial role in accurate land-cover classification and environmental monitoring. However, existing semantic segmentation networks still struggle with multiscale feature extraction and context mod...
Remote sensing-based water body extraction is essential for monitoring hydrological dynamics, particularly in reservoirs with pronounced seasonal variability. This study evaluates automated surface water identification using multi-sensor satellite da...
With the advancement of synthetic aperture radar (SAR) sensor technology, linear structures such as building facades have become increasingly discernible in SAR imagery. Accurate detection of these line features is critical for object recognition and...
Building height is a critical parameter for urban analysis, yet accurately estimating it from ICESat-2 photon-counting LiDAR data remains challenging due to pervasive noise photons and uneven noise distribution. To address the limitations of fixed-th...
The accurate interpretation of land cover changes in multi-temporal satellite imagery is critical for Earth observation. However, existing methods typically yield static outputs—such as binary masks or fixed captions—lacking interactivity...
Micro-motion features of targets, such as nutation and coning, play a crucial role in radar-based target recognition and classification. This paper addresses the challenge of extracting three-dimensional micro-motion parameters from smooth symmetric...
Accurate diagnosis of nitrogen status is essential for precision fertilization in winter wheat. Single-modal or single-temporal remote sensing often fails to capture the multidimensional crop responses to nitrogen stress. In this study, we propose a...
Satellite precipitation products (SPPs) generally exhibit varying accuracy and error characteristics, which influence their applicability in hydrological modeling. Based on gauge-observed precipitation and streamflow data, as well as runoff simulatio...
Clouds critically influence Earth’s radiation balance and climate, making accurate cloud detection essential for improving climate models. This study develops the TSAR model to improve the cloud detection accuracy of the FY-4A CLM product by in...
Soil organic carbon (SOC) plays a critical role in the terrestrial carbon cycle, yet its spatial patterns and drivers in arid regions remain poorly understood. This study aims to clarify SOC distribution mechanisms in the Akesai region, where limited...
Reliable knowledge of power transmission tower locations is fundamental for large-scale inspection and asset management in modern power grids. However, in satellite and aerial remote sensing imagery, towers typically appear as small, slender structur...
The Southwest China vortex (SWV) is a high-impact mesoscale cyclonic vortex that typically originates over Sichuan Province, China, and frequently produces hazardous rainfall. Yet systematic knowledge of the structural and microphysical properties of...
To address the slow convergence and sensitivity to a low signal-to-noise ratio (SNR) of the minimum-entropy autofocus (MEA) algorithm in the refocusing of moving targets, this paper proposes a sparsity-assisted minimum-entropy autofocus algorithm. Wi...
Super-resolution (SR) of remote sensing images (RSIs) is essential for advanced image analysis, yet its progress is challenged by the ill-posed nature of SR and the geometric displacement errors commonly found in paired low-resolution (LR) and high-r...
Pedestrian detection from unmanned aerial vehicles (UAVs) holds significant value in security surveillance and emergency response applications. While visible-infrared (RGB-IR) fusion technology demonstrates potential in handling complex lighting cond...
Surface subsidence has grown to be a major geological problem for big and medium-sized cities in the context of urbanization and climate change. Changchun, a city of moderate size and rapid development, was chosen as the study region for this project...
Unmanned aerial vehicles (UAVs) are increasingly used for crop monitoring, but their widespread adoption is limited since they often rely on non-standard specialized cameras equipped with near-infrared (NIR) sensors. More affordable and scalable crop...
Current synthetic aperture radar automatic target recognition (SAR ATR) tasks face challenges including limited training samples and poor generalization capability to novel classes. To address these issues, few-shot class-incremental learning (FSCIL)...
Deep learning–based pansharpening methods for remote sensing have advanced rapidly in recent years. However, current methods still face three limitations that directly affect reconstruction quality. Content adaptivity is often implemented as an...
The Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Camera (NAC) and Korea Pathfinder Lunar Orbiter (KPLO) ShadowCam provide high-resolution (0.5–2 m per pixel) images of the Moon. These high-resolution images facilitate the creation of...
Accurate fine-scale forest mapping is fundamental for ecological monitoring and resource management. While deep learning semantic segmentation methods have advanced the interpretation of high-resolution UAV imagery, their generalization across divers...
High resolution underwater mapping is fundamental to the sustainable development of the blue economy, supporting offshore energy expansion, marine habitat protection, and the monitoring of both living and non-living resources. This work presents a po...
Transportation infrastructure is vulnerable to heterogeneous deformation, yet such deformation remains insufficiently monitored and characterized in metropolitan regions due to the lack of high-resolution deformation gradient products and comparison...
Remote sensing image captioning (RSIC) aims to generate natural language descriptions for the given remote sensing image, which requires a comprehensive and in-depth understanding of image content and summarizes it with sentences. Most RSIC methods h...
Accurate semantic segmentation of high-resolution remote sensing imagery is crucial for applications such as land cover mapping, urban development monitoring, and disaster response. However, remote sensing data still present inherent challenges, incl...
Remote sensing solutions for avalanche segmentation and mapping are key to supporting risk forecasting and mitigation in mountain regions. Synthetic Aperture Radar (SAR) imagery from Sentinel-1 can be effectively used for this task, but training an e...
Multi-label remote sensing scene classification (MLRSSC) requires autonomous discovery of all relevant land-cover categories without human guidance. Conventional expert classifiers return only label vectors without spatial evidence, while foundation...
Hyperspectral anomaly detection (HAD) aims to identify pixels that significantly differ from the background without prior knowledge. While deep learning-based reconstruction methods have shown promise, they often suffer from limited feature represent...
Landslide susceptibility maps (LSMs) are crucial for risk mitigation, but integrating Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) data is often hampered by a lack of physical interpretation. To address this issue, this study pr...
Transformer-based deep learning techniques have recently shown outstanding potential in remote sensing scene classification (RSSC), benefiting from their ability to capture global semantic relationships and contextual dependencies. However, effective...
Accurate crop type mapping remains challenging in regions where persistent cloud cover limits the availability of optical imagery. Multi-temporal dual-polarization Sentinel-1 SAR data offer an all-weather alternative, yet existing approaches often un...
This study, conducted in the framework of the LIAISE field campaign in NE Spain (May–September 2021), investigates how near-surface relative humidity influences early-stage rainfall characteristics when precipitation is most affected by tempera...
Evapotranspiration (ET) links the water cycle with the energy balance and serves as a key driving process for ecosystem functioning and water resource management. Canopy conductance (Gc) plays a central role in regulating transpiration, but many mode...
Airplanes are the most popular investigation objects as a dynamic and critical component in remote sensing images. Accurately identifying and monitoring airplane behaviors is crucial for effective air traffic management. However, existing methods for...
The lack of high-precision imaging data for lunar volcanic regions currently hinders the detailed characterization of lava tube systems and their associated fine-scale geomorphology. To address this information deficit, this study establishes the Jin...
Video synthetic aperture radar could provide more valuable information than static images. However, it suffers from several difficulties, such as strong clutter, low signal-to-noise ratio, and variable target scale. The task of moving target detectio...
The concentrations of carbon, nitrogen, and phosphorus in water bodies significantly influence aquatic ecological conditions. By collecting multitemporal hyperspectral data and water quality parameter data from water bodies and through systematic pre...
This study evaluates the performance of artificial intelligence (AI) technologies for wetland classification in the province of Alberta, Canada, using integrated remote sensing inputs, including airborne light detection and ranging (LiDAR), orthophot...
As a key technology in the intelligent interpretation of remote sensing, remote sensing image change detection aims to automatically identify surface changes from images of the same area acquired at different times. Although vision foundation models...
This work proposes a novel tree detection methodology, named DTCD (Distance Transform Circle Detection), based on a fast circle detection method via Distance Transform and Akaike Information Criterion (AIC) optimization. More specifically, a visible-...
Sub-band errors are inevitable in synthetic bandwidth synthetic aperture radar (SAR) systems due to differences in signal paths and frequency responses of the components used for different sub-bands, which degrade imaging performance if not properly...
Disentangling the impacts of ecological restoration from climate change is an ongoing challenge in remote sensing since the traditional correlative approaches often cannot elucidate causal mechanisms. To overcome this, we introduce a Causal Remote Se...
The widespread adoption of camera trap surveys for wildlife monitoring has generated a substantial volume of ecological data, yet processing constraints persist due to the time-consuming process of manual image classification and the reliability of a...
Eutrophication is a form of pollution caused by elevated nutrient concentrations in water bodies, leading to excessive algal growth and subsequent oxygen depletion. This process poses significant risks to aquatic ecosystems and overall water quality....
Precise characterization of the thermospheric neutral wind is essential for comprehending the dynamic interactions within the ionosphere-thermosphere system, as evidenced by the development of models like HWM and the need for localized data. However,...
Italy is estimated to host thousands of abandoned mines, many of which contain large volumes of mine residues that negatively affect land and aquatic ecosystems, also posing a risk to human health. This study evaluates the effectiveness of spaceborne...
Remote sensing image scene classification holds significant application value and has long been a research hotspot in remote sensing. However, remote sensing images contain diverse objects and complex backgrounds. Reducing background interference whi...
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