Rapid urbanization and global climate change are intensifying the Urban Heat Island (UHI) effect in cities worldwide, with consequences for human health and well-being. Urban green spaces (UGSs) mitigate extreme temperatures, but their cooling potential depends on spatial configuration, size, shape, and distribution. This study fills a geographic gap by providing one of the first detailed analyses

China, despite being a leading producer of potatoes, has a potato yield below the global average, primarily due to inefficient nutrient management practices. Remote sensing provides a non-invasive and large-scale approach to monitor crop nutrient status, offering an efficient alternative to traditional plant tissue analysis. However, the generalization of foliar nutrient models is often constrained

The integration of Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) data with Optical Photogrammetric Satellite Stereo Imagery (OPSSI) for Block Adjustment (BA) has emerged as a novel approach for generating large-area, high-accuracy Digital Elevation Models (DEMs). However, owing to the discrepancies between these two data platforms and the systematic errors of their sensors, errors arise in

Satellite video, as an emerging data source for Earth observation, enables dynamic monitoring and has wide-ranging applications in diverse fields. Nevertheless, cloud occlusion hinders the ability of satellite video to provide uninterrupted monitoring of the Earth’s surface. To mitigate the interference of clouds, cloud-free areas need to be selected before application, or an optimized solution like

Scientific workflow management systems (WfMS) provide a systematic way to streamline necessary processes in scientific research. The demand for FAIR (Findable, Accessible, Interoperable, and Reusable) workflows is increasing in the scientific community, particularly in GIScience, where data is not just an output but an integral part of iterative advanced processes. Traditional WfMS often lack the capability

High-resolution (≤10 m) digital elevation models (DEMs) are essential for obtaining accurate terrain information and are integral to geographic analysis. However, a majority of currently available DEMs datasets possess a relatively coarse spatial resolution (≥30 m), which limits the terrain features and details that can be accurately represented. Furthermore, due to the substantial production costs

Accurate measurement of high-resolution snow depth (SD) is crucial for regional ecohydrology and climate studies. Passive microwave remote sensing is an effective technique for SD retrieval on global or regional scales. However, its low spatial resolution limits its application in various fields. Additionally, the complex effects of multiple factors in the microwave radiation process pose a significant

Tidal flats and their associated sandbanks are dynamic environments crucial for ecological balance and biodiversity. Monitoring their evolutionary history and topographic changes is important to better understand their dynamic mechanisms and predict their future status. Accurately mapping their evolution, however, remains challenging due to highly dynamic currents, suspended sediment variability, and

This study employs an integrated approach, combining remote sensing and numerical modelling techniques, to characterize flood-prone regions resulting from the combined effects of extreme river water elevations and long-term sea-level rise in the Senegal River Estuary. Four different case scenarios of hydrodynamic conditions have been investigated to provide a quantitative assessment of flooding. Simultaneously

Accurate assessment of fuel conditions is a prerequisite for fire ignition and behavior prediction, and risk management. The method proposed herein leverages diverse data sources – including L8 optical imagery, S1 (C-band) Synthetic Aperture Radar (SAR) imagery, PL (L-band) SAR imagery, and terrain features – to capture comprehensive information about fuel types and distributions. An ensemble model

The spatially continuous dynamic monitoring of near-surface NO2 concentrations on sub-daily scales would serve to enhance awareness of the current state of air pollution, which is crucial to improving regional air quality. Satellites, like OMI and TROPOMI, are capable of observing atmospheric NO2 column concentrations on a global scale. However, the fixed transit times of the satellites and severe

Estimating deformation at the upstream dam slope from Interferometric Synthetic Aperture Radar (InSAR) is challenging due to the complete loss of coherence in seasonally inundated upstream slope. Here, we present an improved Distributed Scatterer-InSAR method that accounts for the seasonal decorrelation of upstream dam slopes and optimizes the interferogram pair selection with inter- and multi-annual

Monitoring and governing the development of cities are the major concerns of urban planners, since involve physical and social aspects, such as land use and population trends. Models for spatial growth have been developed both from the mathematical and empirical viewpoints, with the aim of forecasting and decision-making. Statistical models require regular space–time datasets that are provided by recent

Land surface temperature (LST) is an indispensable factor for comprehending of surface equilibrium state on the Earth. In particular, satellites can continuously provide LST data and support the large-scale monitoring of LST with a high temporal resolution; however, satellite data may be easily contaminated by clouds. Previous satellite-based all-sky LST reconstruction approaches have inherent limitations

Information regarding the spatial arrangement and extent of past habitats is important for understanding present biodiversity, restoration potential, and fighting extinction-debt effects. European landscapes have changed profoundly over recent decades, with the trend accelerating following World War 2. We develop a proof of concept for mapping historic habitat distribution for Switzerland from black

Soil texture is an essential attribute of soil structure, which plays an important role in evaluating soil fertility and carrying out agricultural production. This study developed a novel soil texture estimation model using ZiYuan-1-02D (ZY1-02D) satellite Advanced Hyperspectral Imager (AHSI), based on the mechanism of soil spectral mixing, that enables simultaneous estimation of the three soil texture

At present, there are more than 30 satellite remote sensing Land Surface Temperature (LST) products from kilometers to hectometers resolutions. The accuracy of these products is the key issue for further application. The validation of LST products is mainly achieved through ground observations on homogeneous surfaces, but the accuracy of satellite products on heterogeneous surfaces is also an important

Existing scene classification methods allocate the same computational resources, i.e., all model parameters in the neural network, to each remote sensing image whenever from any geographic scene. However, this might be redundant for images of certain scenes that are easy to discriminate, e.g., homogeneous scenes. This observation motivates us to propose an efficient method for on-orbit scene classification

With the persistent rise in intensity and magnitude of hydrological extremes globally, timely information from operational early flood warning systems provide lead times that translate into actionable strategies to monitor and mitigate flood risk. However, the situation is often different for flood-prone regions of the global south with sparse to no ground flood monitoring systems, where flood management

Airborne hyperspectral systems can provide high-resolution hyperspectral images (HSIs) covering large scenes, enabling fine-grained land-cover classification. However, the most popular patch-based methods are limited by low computational efficiency and broken classification results, which hinders the full utilization of this powerful technology in Earth observation applications. Therefore, in this

The p-Median Problem (PMP) is a classical discrete facility location problem with significant implications for optimizing the placement of urban public service facilities. Improved heuristics, a well-established method for solving the PMP, aim to iteratively enhance solution quality through efficient neighborhood exploration. In this study, we model the neighborhood exploration process as a Markov

Deep learning methods for classification have achieved significant advancements in processing 3D point clouds. A fundamental aspect of deep learning networks is how to best aggregate features into a global representation of the point cloud. While many existing networks rely on the traditional max-pooling for feature aggregation due to its efficiency and permutation-invariance, max-pooling has some

The spatiotemporal characteristics of remote sensing data are often time-varying, leading to significant fluctuation and instability in tree species classification results across different years, especially in regions referred to as high-variance areas. To improve the stability and accuracy of the classification results, this study proposes a lightweight spatiotemporal classification framework, with

Maritime transportation plays a pivotal role in global trade, making maritime transportation safety a longstanding priority within the maritime industry. With the growing emphasis on big data and geospatial analysis in maritime safety management, this study presents a comprehensive review of 425 academic publications on the topic from 2004 to 2023. First, publication trends, influential journals, and

Consideration of spatial dependence in heterogeneous urban landscapes is crucial for understanding how urban landscapes shape biodiversity. However, understanding the linkage between urban landscape patterns, both vertically and horizontally, and urban-dwelling bird species at various spatial scales remains an unsolved question. Here, we investigated how patterns of vertical and horizontal urban landscape

Accurate building height information is essential for urban management and planning. However, most existing methods rely on general segmentation networks for building height estimation, often ignoring the structural characteristics of buildings. This paper proposes a novel structure-aware building height estimation (SBHE) model to address this limitation. The model is designed as a dual-branch architecture:

Forest fires significantly threaten Indonesia’s tropical forests, driven by complex interactions between human activity, environmental conditions and climate variability. This research aims to identify and analyze the factors influencing forest fires in Kalimantan, Sumatra, and Papua during the rainy, dry and all-season conditions using machine learning techniques and create vulnerability prediction

Satellite video super-resolution (SR) aims to generate high-resolution (HR) frames from multiple low-resolution (LR) frames. To exploit motion cues under complicated motion patterns, most CNN-based methods first perform motion compensation and then aggregate motion cues in aligned frames (features). However, due to the low spatial resolution of satellite videos, the moving scales are usually subtle

Irrigation uniformity is a key factor in optimizing water use efficiency and maximizing crop yields, particularly in semi-arid regions. This study investigates the use of high-resolution unmanned aerial vehicle (UAV) thermal and visible light imagery, to assess irrigation uniformity in three systems: surface, linear move, and solid-set irrigation. The research aims to quantify irrigation variability

The 2023 Mw 6.8 Al Haouz earthquake struck Morocco’s Atlas Mountains on September 8, causing over 3000 fatalities and extensive damage, revealing hidden seismic hazards in this slowly deforming region. Despite its impact, Al Haouz earthquake has received limited scientific investigation. The absence of surface rupture, its occurrence in an intraplate seismic silence zone, and ambiguous focal mechanisms

Geological lithological interpretation is a key focus in Earth observation research, with applications in resource surveys, geological mapping, and environmental monitoring. Although deep learning (DL) methods has significantly improved the performance of lithological remote sensing interpretation, its accuracy remains far below the level achieved by visual interpretation performed by domain experts

Semantic segmentation of remote sensing imagery is a fundamental task to generate pixel-wise category maps. Existing deep learning networks rely heavily on dense pixel-wise labels, incurring high acquisition costs. Given this challenge, this study introduces sparse point labels, a type of cost-effective weak labels, for semantic segmentation. Existing weakly-supervised methods often leverage low-level

In the fields of computer graphics, computer vision and photogrammetry, Neural Radiance Fields (NeRFs) are a major topic driving current research and development. However, the quality of NeRF-generated 3D scene reconstructions and subsequent surface reconstructions, heavily relies on the network output, particularly the density. Regarding this critical aspect, we propose to utilize NeRF-Ensembles that

The diversity of building functions is vital for urban planning and optimizing infrastructure and services. Street-view images offer rich exterior details, aiding in function recognition. However, street-view building function annotations are limited and challenging to obtain. In this work, we propose a geometry-aware semi-supervised method for fine-grained building function recognition, which effectively

Knowledge graphs (KGs) have gained significant attention in the GIS community as a cutting-edge technology for linking heterogeneous and multimodal data sources. However, the efficiency of semantic querying of geospatial-temporal data in KGs remains a challenge. Graph databases excel at handling complex semantic associations but exhibit low efficiency in geospatial analysis tasks, such as topological

Accurate crop mapping plays a critical role in optimizing agricultural monitoring and ensuring food security. Although data-driven deep learning methods have demonstrated success in crop mapping with satellite image time series (SITS) data, their promising performances heavily depend on labeled training samples. Nevertheless, the difficulty of annotating crop types often results in labeled data scarcity

Climate change has led to stronger warming in the Arctic, causing higher ground temperatures and extensive permafrost thaw. Retrogressive Thaw Slumps (RTSs) represent one of the most rapid and considerable geomorphological changes in permafrost regions, occurring when ice-rich permafrost is exposed and thaws. However, large-scale quantification of RTS-related mass wasting in Arctic permafrost landscapes

Particulate organic carbon (POC) is fundamental to the marine carbon cycle, yet accurately estimating its concentration from satellite data remains challenging. In this study, we developed a novel machine learning framework that incorporates multiple data streams, covering apparent and inherent optical properties, biological indicators, and environmental variables, to improve global POC retrieval.

Timely and accurate geolocalization of natural disasters is crucial for effective emergency response, which is foundational for risk mitigation and resilience development. Although social media texts have been widely used to recognize and resolve disaster geolocations, the implicit geoinformation in social media images remains largely underexplored. In this paper, we propose a novel large language

Retrogressive thaw slumps (RTSs) are severe manifestations of permafrost degradation with profound implications for regional environments and ecosystems. Previous studies heavily rely on high-resolution imagery and deep learning methods for RTS mapping. However, the acquisition of high-resolution imagery and the extensive computation of the deep learning-based method present challenges for long-term

Fueled by both terrestrial and marine inputs, estuaries worldwide are important biogeochemical reactors, strongly susceptible to natural and anthropogenic, episodic, or compounding disturbances. Satellite sensors provide a unique vantage point to capture estuarine processes at scales not feasible with in situ sampling alone; yet, remote sensing retrievals of estuarine biogeochemical dynamics remain

Remote sensing observations of green vegetation (GV), impervious surface (IS), and bare soil (BS) fractional cover are essential for understanding climate change, characterizing ecosystem functions, monitoringurbanization process. As an important indicator of urbanization, the continuous increase of impervious surfaces alters the radiative transfer process at the surface, causing a series of environmental

Gully erosion cause soil organic matter loss, which poses a grave threat to food security and regional ecological sustainability. Remote sensing monitoring and information extraction of gully erosion are of great significance to protect cultivated land resources and agricultural production. To improve the extraction accuracy of gully erosion, multi-level random forest (RF) extraction model based on

This paper Introduces a novel method for Polarimetric Synthetic Aperture Radar (PolSAR) image classification using a Complex-Valued Multiscale Attention Vision Transformer (CV-MsAtViT). The model incorporates a complex-valued multiscale feature fusion mechanism, a complex-valued attention block, and a Complex-Valued Vision Transformer (CV-ViT) to effectively capture spatial and polarimetric features

With the increasing popularity of sensors and the rapid advancement of network infrastructure and communication technology, managing, retrieving, and applying ubiquitous location information (ULI) poses a significant challenge. This study introduces the concept of the geospatial grid region name (GGRN) and proposes a ULI management method based on the GGRN (UMMG). To evaluate the feasibility and retrieval

Silty Midstream Yellow River (MYR), characterized by its turbid waters, is currently underserved by a sparse network of gauging stations, which is insufficient for comprehensive flow monitoring. Establishing an extensive gauging network in this region is almost impractical. This study addresses the challenge by estimating discharge at selected ungauged reaches of the MYR, leveraging multiple remote

Plantation forests have been increasingly established in Fagita Lekoma District, located in the Northwestern Highlands of Ethiopia, over the past two decades. However, their interaction with climate variables remains largely unexplored. This study aims to investigate the spatiotemporal dynamics of plantation forests driven vegetation changes and their relationship with climate variables in the district

Soil freeze–thaw (F/T) processes are a typical physical phenomenon on the Qinghai-Tibetan Plateau (QTP), significantly impacting regional climate change and the hydrological cycle. This study presents a Seasonal-Trend Decomposition using Loess and Long Short-Term Memory (STL-LSTM) method to detect spatiotemporal variations in soil F/T on the QTP using time series data from the Cyclone Global Navigation

Geo-registration is a fundamental process seamlessly integrating digital information within the physical world in Mobile Augmented Reality (MAR). Achieving high precision, real-time capability, and strong adaptability in geo-registration is crucial for the effective functioning of MAR applications, especially in outdoor environments. However, existing methods frequently struggle with inaccuracies in

In recent years, the precise identification of an insect pest infestation has become increasingly critical for effective management in agricultural fields. This research addresses the imperative need for an advanced and integrated approach to mapping insect pest infestation in agricultural crops, utilising unmanned aerial vehicles (UAVs), multispectral (MS) imagery, and deep learning (DL). The existing

We describe our work to improve the cloud masking for satellite ocean color data processing over extremely turbid waters and intensive algae blooms (or floating algae), which are often identified as cloud mistakenly. An improved cloud masking approach is proposed using additional information of the Alternate Floating Algae Index (AFAI) and a new normalized AFAI (nAFAI), as well as ratios of the Rayleigh-corrected

Climate change and recurrent extreme climatic events have intensified the vulnerability of water-stressed regions like Tunisia to droughts, severely impact agriculture, the economy, and society. This study analyzes hydro-meteorological drought patterns using the Gravity Recovery and Climate Experiment (GRACE) satellite-derived Groundwater Drought Index (GGDI), alongside traditional indices, including

High-spatiotemporal-resolution sea surface salinity (SSS) estimations are essential for understanding marine phenomena in both coastal seas and open oceans. Although studies have enhanced the resolution of SSS estimations using ocean color (OC) satellite data, the limited variance of OC signals and weak correlation with SSS in open oceans have confined these advancements to coastal seas. To overcome

Leaf stomatal conductance (Gs) is an important indicator for measuring crop water stress. Influenced by variation of environmental conditions and growth stages of crops, achieving the reliable and accurate Gs estimation by UAV image is of challenge. Therefore, this study aimed to explore the potential of Gs estimation of winter wheat by UAV-based multispectral imagery based on coupling meteorological

Surveillance is crucial for maintaining critical infrastructure integrity and disaster risk reduction. Unmanned Aerial Vehicles have emerged as vital tools for aerial inspections, offering flexibility, efficiency, and cost-effectiveness. A significant challenge in UAV surveillance is the precise detection of damaged electrical components, particularly in complex environments where numerous objects

Wildfires pose a great threat to the ecological environment and human safety. Therefore, rapid and accurate detection of wildfires holds significant importance. However, existing wildfire detection methods neglect the full integration of spatial–temporal relationships across different scales, and thus suffer from issues of low robustness and accuracy in varying wildfire scenes. To address this, we

Changes in cropland are among the most widespread transitions on the Earth surface, significantly impacting food security, ecological conservation, and social stability. Compared to conventional change events, cropland changes involve complex dynamic transformations of semantic representations within the land system, requiring the identification of both the locations and categories of changes. Despite

Ultraviolet (UV) remote sensing plays a critical role in understanding photochemical and biological processes in the global ocean. While UV radiation significantly influences the marine environment, the limited availability of global UV measurements has hindered comprehensive analyses, particularly in photochemically sensitive regions. The Ultraviolet Imager (UVI) on China’s HaiYang-1C (HY-1C) satellite