Due to the significant disparities in feature spaces of multi-source images, change detection (CD) of heterogeneous remote sensing images (HRSIs) remains a highly challenging problem. Currently, CD methods based on domain transfer networks (DTNs) have garnered significant attention. However, the computer scientists underutilize knowledge in the field of CD during DTNs design, and the existing CD methods

We investigate the problem of 3D point clouds semantic segmentation. Recently, a large amount of research work has focused on local feature aggregation. However, the foundational framework of semantic segmentation of 3D point clouds has been neglected, where the majority of current methods default to the U-Net framework. In this study, we present U-Next, a small but mighty framework designed specifically

Bamboo forests are natural habitat for the giant panda which is one of the most vulnerable mammal species. In structurally complex natural forests, bamboos are normally located under the canopy of taller trees, which makes them difficult to be quantified accurately. Although Light Detection and Ranging (LiDAR) technologies have been well established as the effective tool for forest structure assessment

The boreal forest surrounds the Arctic region and is the most extensive ecosystem on Earth; one-third of its soil is influenced by permafrost and accompanying wetlands. Interferometric Synthetic Aperture Radar (InSAR) technology has been widely utilized to monitor ground surface deformation in Arctic tundra and alpine grassland permafrost environments; however, its application in boreal forest areas

Shallow water environmental information is crucial for the study of marine ecosystems and human activities. There have been numerous satellite remote sensing studies focused on this area. However, accurate information acquisition from remote sensing data remains difficult in this region due to the complexity of the environment and the coupling between benthic reflectance and water column scattering

Satellite images have been adopted in recent years for identifying topographical features on the Earth’s surface. Researchers have also published reports on the use of satellite images to analyze shoreline changes or to verify shoreline change in numerical models. But reports that demonstrate the reverse process of using satellite images to estimate the incident waves to a beach are rare, particularly

A wide variety of spatially explicit modeling algorithms has recently mushroomed in geoinformation research. These algorithms establish local models with data from spatially confined subsets, thereby offering a new impetus for addressing the issue of spatiotemporal non-stationarity. However, a significant challenge persists in literature that local models are primarily predicated on linear assumptions

Domain adaptation is crucial for information integration of remote sensing systems, such as satellite constellations and space stations, to intelligently achieving full domain awareness. The conventional methods focus on aligning spatial features without fully considering the topological structure and semantic information in the scene, resulting in loss of useful information and suboptimal classification

Accurate, rapid, and automated landslide detection is crucial for early warning, emergency management, and landslide mechanism analysis. Increasingly general-purpose detection models are being deployed for these complex and dynamic tasks involving features that are difficult to characterize. However, these models are computationally expensive and memory-hungry, while the accuracy and detection efficiency

Land use/land cover (LULC) is one of the most impactful global change phenomenon. As a result, considerable effort has been devoted to creating large-scale LULC products from remote sensing data, enabling the scientific community to use these products for a wide range of downstream applications. Unfortunately, uncertainty associated with these products is seldom quantified because most approaches are

Building generalization is an essential task in generating multi-scale topographic maps. The progress of deep learning offers a new paradigm to overcome the coordination challenges faced by conventional building generalization algorithms. Some studies have confirmed the feasibility of several original semantic segmentation networks, such as U-Net and its variants and the conditional generative adversarial

Effectively leveraging landslide spatial location information is crucial for improving the accuracy of deep learning in predicting landslide susceptibility and exploring the impacts of predisposing factors. Current single deep learning models for landslide susceptibility assessment require enhancements in both prediction accuracy and robustness. Inclusion of non-interrelated positional information

Glacier velocity is one of the crucial parameters in the research of glacier dynamics. Synthetic aperture radar (SAR), as an active microwave sensor, represents a common method to monitor glacier velocity. However, the changes of glacier surface could cause the data missing of glacier velocity due to incoherence. To meet the demand for glacier velocity monitoring, this paper employs the SAR images

Under the background of climate change and global warming, extreme drought events in China are becoming increasingly frequent. Drought is one of the primary natural causes of damage to China’s agriculture, economy, and environment, making timely, accurate, and high-resolution drought monitoring particularly crucial. The global standardized precipitation − evapotranspiration index database (SPEIbase)

Recently Guangzhou and Foshan in China are experiencing significant urbanization and economic development. However, the accelerated urbanization process has contributed significantly to urban land subsidence, causing huge economic losses and endangering safety of infrastructure. This intricate activities on urban surfaces can also lead to pseudo danger in interpreting InSAR-based urban surface deformation

Long-term climate change significantly affects the spatiotemporal dynamics of soil erosion. To explore this, remote sensing technology, future climate scenarios, and deep learning are combined to model the historical and future variations in soil erosion, investigating its spatiotemporal dynamics influenced by climate change. This paper uses the Revised Universal Soil Loss Equation (RUSLE) to assess

Active landslides pose significant global risks, underscoring precise displacement monitoring for effective geohazard management and early warning. The Three Gorges Reservoir Area (TGRA) in China, a pivotal section of the world’s largest water conservancy project, has developed thousands of landslides due to unique hydrogeological conditions and reservoir operations. Many of these landslides are oriented

Sunlit and shaded components are commonly present in both airborne and satellite remote sensing images. In vegetated ecosystems, shaded component often result from sunlight being obstructed by topographic relief or canopy structures, and shaded component may impact plant growth, leaf photosynthesis, and ultimately carbon sequestration. To accurately estimate the fractional cover of the shaded and sunlit

Vegetation carbon sequestration is a fundamental process that supports ecosystem biodiversity and ecological services. It is a key factor in shaping ecosystem state and energy flow. Global climate change has intensified in recent years. Frequent drought events affect the stabilization of carbon cycle. In this study, we used correlation analysis method to explore the relationship between standardized

Understanding terrestrial vegetation phenology—the timing of life-cycle events—is crucial for insights into ecosystem energy and material cycles. Land surface phenology (LSP) derived from satellite observations has become a critical tool for tracking vegetation phenology across large spatial scales. However, LSP data from coarse spatial resolutions often mix phenological signals from multiple land

The efficient and automated identification of landslide hazards is essential for socio-economic development and human safety. Integrating the feature extraction capabilities of deep learning with the millimeter-level precision of Interferometric Synthetic Aperture Radar (InSAR) technology establishes a foundation for this task. However, current methods require unwrapping interferograms, and even converting

Hyperspectral images (HSIs) acquired from different imaging platforms are inevitably contaminated by multiple types of noise. However, the existing supervised learning based denoising methods often show poor generalizability on data with complex degradation, due to the discrepancy between synthetic training data and real data. Although some unsupervised denoisers have been developed to learn priors

Severe surface deformation can damage the ecological environment, trigger geological disasters, and threaten human life and property. Reliable surface deformation prediction is conducive to reducing potential risks and mitigating disaster losses. Currently, machine learning-based surface deformation prediction models have shown significant improvements in prediction performance. However, most prediction

This study aimed to analyze the spatiotemporal concentration of air pollutants in the tropospheric layer of Jordan, in the Middle East, for 2019–2022. The study utilized remotely sensed data from two satellite systems, Sentinel-5P and Landsat-9, to retrieve information about the concentration of nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO) and land use types, respectively

The increasing use of smartphones has led to a surge in crowdsourcing initiatives, where citizens easily collect and upload information using advanced sensors, leveraging the collective efforts of the crowd. However, these devices face accuracy issues that must be addressed before they can be used effectively in certain applications. While most research has focused on GNSS-based positioning errors

Surface soil moisture (SM) plays an important role in water and energy cycles. Passive microwave remote sensing observation has become the main means of obtaining large-scale surface SM. Due to its low spatial resolution, the spatial downscaling is required. With the development of artificial intelligence, data-driven SM downscaling models have emerged in recent years and have shown better accuracy

The phenological characteristics of vegetation exposed to oil pollution can reveal how different vegetation types and species respond to the effects of hydrocarbons in crude oil. This can further inform the recovery status and remediation efforts on polluted sites. In this study, the potential of various SAR frequencies (including multitemporal C band Sentinel-1, X band Cosmo Skymed, X band TanDEM-X

Inland freshwaters are essential in maintaining ecological balance and supporting human development. However, comprehensive water data cataloguing remains insufficient, especially for small water bodies (i.e., ponds), which are overlooked despite their ecological importance. To address this gap, remote sensing has emerged as a possible solution for understanding ecohydrological characteristics of water

This study aimed to create a model to identify land suitable for growing sunn hemp (Crotalaria juncea) and pigeon pea (Cajanus cajan) in the Okavango River basin of the Kavango East region of Namibia. Advanced tree-based ensemble learning models, including Random Forest, Extra Trees, Gradient Boosting, XGBoost and multivariate regression analysis , were employed to enhance analytical accuracy. The

This paper addresses the challenge of indoor space segmentation from 3D point clouds, which is essential for understanding interior layouts, reconstructing 3D structures, and developing indoor navigation maps. While current deep learning-based methods rely on projecting 3D point clouds into 2D for instance extraction, they often fail to capture the local and global 3D features necessary for effectively

Leaf chlorophyll content (LCC) retrieval from remote sensing imagery is essential for monitoring vegetation growth and stress in the agroforestry industry. Many remote sensing inversion methods for estimating LCC primarily rely on 1D radiative transfer models (RTMs) that abstract canopies into horizontal layers or simple geometric primitives. Yet, this methodology faces challenges when applied to heterogeneous

Wheat stripe rust is a significant disease affecting wheat growth, often referred to as the “cancer of wheat”. Early and accurate detection of stripe rust is crucial for enabling crop managers to implement effective control measures. Hyperspectral remote sensing methods for crop disease detection have gained significant attention. However, commonly used spectral bands or spectral indices (SIs) from

Nitrogen is one of the most important macronutrients for plant growth and timely estimation of canopy nitrogen content (CNC) is crucial for agricultural applications. Remote sensing has emerged as an important tool to quantify CNC using either empirically or physically based methods. Most empirical methods use chlorophyll related spectral indices and are dependent on the relationship between nitrogen

Due to policies such as groundwater extraction restrictions, water diversion, and water replenishment, groundwater levels in the Beijing Plain have generally risen. This has effectively alleviated ground subsidence, with some regions even experiencing uplift. Under these new water conditions, strata deformation also shows spatiotemporal heterogeneity, and the overlap of these multiple deformation patterns

Accurate geolocation using Global Navigation Satellite Systems (GNSS) is essential for safe and long-range unmanned aerial vehicles (UAVs) flights. However, GNSS systems are susceptible to blockages, jamming, and spoofing attacks. Localization using onboard cameras and satellite images provides a promising solution for UAVs operating in GNSS-denied environments. In this paper, we developed a novel

Snow cover significantly influences the Earth’s climate system and global hydrological cycle through its thermal insulation properties and high albedo, and is an important component of the cryosphere. Currently, the most efficient means of quantifying snow depth at both global and regional scales is through passive microwave remote sensing. However, the accuracy of passive microwave remote sensing

Interpreting surface geological elements (such as rocks, minerals, soils, and water bodies) is the main task of geological survey, which plays a crucial role in geological environment remote sensing (GERS). However, the characteristics of geological elements, including high variabilities, various morphology, complicated boundaries and imbalanced class distribution, make it still a challenge for deep

Ocean colour (OC) remote sensing benefits society by providing continuous biological and ecological parameters relevant to sustainable marine resource exploitation. It enhances our understanding of climate change and allows us to monitor oceanographic phenomena over various scales of variability. However, significant data gaps occur daily due to cloud cover, atmospheric correction failures, sun-glint

Synthetic aperture radar (SAR) has developed in leaps and bounds over the past decades, which makes rapid revisit and high-frequency coverage feasible. However, accurate and efficient registration of the SAR image is still a challenging task. Many existing SAR image registration methods major in describing detected features in a unique, identifiable, but maybe complex way. These descriptors are usually

Due to rapid urbanization and globalization, urban functions are increasingly segregated in cities comprising centers of population aggregation and economic activity. Urban development yields intertwining and interdependent functional areas for residence, commerce, and education, which leads to complex but regular population mobility patterns. Population mobility spaces can effectively represent the

Due to constraints in data and technological approaches, there is a deficiency in the analysis of spatial patterns and formation mechanisms of large-scale destination tourist flows at the provincial level. This study leverages open GPS trajectory big data and employs grid units to meticulously characterize the spatial patterns and associated formation mechanisms of regional-scale tourist flows in Qinghai

Equitable internet coverage has emerged as a key global priority, which is essential for promoting inclusive and sustainable development. The Indonesian government aims to provide universal internet access by 2024, particularly in remote regions. This study introduces a novel machine-learning-based approach to identify the priority areas for deploying Base Transceiver Station (BTS) towers, which are

The agrarian economy of Bhutan is highly vulnerable to rainfall uncertainties for its typical geographic location and rugged topography. Rainfall time series is also constrained by inadequate rain gauge stations in the country. To complement rainfall data obtained from 12 distinct satellite sources is validated against surface measurements from 12 ground stations. The rainfall obtained from Precipitation

The localization of non-residential buildings over wide geographical areas is used as input within several contexts such as disaster management, regional and national planning, policy making and evaluation, among others. While the built-up environment has been continuously and globally mapped, given the efforts on producing synoptic land cover information; little attention has been given to the land

Here, a methodology to generate national-scale annual 10 m spatial resolution land use and land cover maps for Fiji (Fiji LULC) is presented. A training dataset of 13,419 points with a LULC label across three years from 2019 to 2021 was generated alongside a nationally representative test dataset of 834 points. These data were used to train a random forests model to convert an image stack of pre-processed

In remote sensing mapping studies, selecting an appropriate test set to accurately evaluate the results is critical. An imprecise accuracy assessment can be misleading and fail to validate the applicability of mapping products. Commencing with the WHU-Hi-HanChuan dataset, this paper revealed the impact of sample size ratios in test sets on accuracy metrics by generating a series of test sets with varying

Impervious surfaces are important indicators of human activity, and finding ways to quantify the gain and loss of impervious surfaces is important for sustainable urban development. However, most relevant studies assume that the transformation of natural surfaces to impervious surfaces is irreversible; thus, the losses of impervious surfaces are often ignored. Here, we propose a novel framework taking

The El Niño Southern Oscillation (ENSO) significantly influences global climate patterns, with one of the strongest warm phases (El Niño) occurring in 2023, altering precipitation and temperature regimes. In this study, the spatiotemporal variability in snow cover across Canadian provinces from December 2023 to February 2024 relative to long-term averages is explored. The NOAA-OISST, NOAA-CSFV2, and

This study investigates the reconstruction of climatological patterns and vegetation dynamics in the Horn of Africa region using high temporal resolution (i.e. daily) Normalized Difference Vegetation Index (NDVI) datasets. The analysis compares a straight-forward processing approach to derive a daily vegetation index from a geostationary (SEVIRI) satellite with existing NDVI series from geostationary

Zanthoxylum rust (ZR) poses a significant threat to Zanthoxylum bungeanum Maxim.(ZBM) production, impacting both the yield and quality. The lack of current research on ZR using unmanned aerial vehicle (UAV) remote sensing poses a challenge to achieving precise management of individual ZBM plant. This study acquired six UAV hyperspectral images to create a ZR inversion dataset . This dataset, to our

With advances in data collection technology, the volume of recent remote sensing point cloud datasets has grown significantly, posing substantial challenges for point cloud deep learning, particularly in neighborhood aggregation operations. Unlike simple pooling, neighborhood aggregation incorporates spatial relationships between points into the feature aggregation process, requiring repeated relationship

Plant foliage is known to respond rapidly to environmental stressors by adjusting leaf orientation at different timescales. One of the most fascinating mechanisms is paraheliotropism, also known as light avoidance through leaf movement. The leaf orientation (zenith and azimuth angles) is a parameter often overlooked in the plant and remote sensing community due to its challenging measurement procedures

The heterogeneity and complexity of multimodal data in high-resolution remote sensing images significantly challenges existing cross-modal networks in fusing the complementary information of high-resolution optical and synthetic aperture radar (SAR) images for precise semantic segmentation. To address this issue, this paper proposes a multi-layer perceptron (MLP) dominated gate fusion network (MGFNet)

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) mission, spanning the past five years, has collected extensive three-dimensional Earth observation data, facilitating the understanding of environmental changes on a global scale. Its key product, Land and Vegetation Height (ATL08), offers global land and vegetation height data for carbon budget and cycle modeling. Consistent measurement accuracy

Traditional methods for measuring asset wealth face limitations due to data scarcity, making it challenging to apply them on a large scale and over long periods with fine granularity. Publicly available satellite images, such as nighttime light imagery, have become an important alternative data source for estimating asset wealth. This study thoroughly exploited the spatial neighborhood information

Supervised classification methods can distinguish between noise and signal in ice, cloud, and land elevation satellite-2 (ICESat-2) data across various feature perspectives and autonomously optimize parameters. Nevertheless, model generalization remains a significant limitation for practical applications. This study focuses on developing a universal denoising model for ICESat-2 using machine learning

Hyperspectral unmixing is of vital importance within the realm of hyperspectral analysis, which is aimed to decide the fractional proportion (abundances) of fundamental spectral signatures (endmembers) at a subpixel level. Unsupervised unmixing techniques that employ autoencoder (AE) network have gained significant attention for its exceptional feature extraction capabilities. However, traditional

The escalating water pollution in many lakes has led to more frequent occurrences of algal bloom disasters in recent decades. The severity of these disasters can be assessed through remote sensing techniques, specifically using the Normalized Difference Vegetation Index (NDVI) for measurement. However, NDVI observations using optical sensors are often affected by cloud and fog in areas with numerous

Invasive alien plant species (IPS) are one of the major threats to biodiversity and ecosystem services. As the dynamics of biological invasions by non-native plant species are expected to intensify with climate change, there is an increasing need to provide accessible information on the distribution of IPS to improve environmental management programmes. Monitoring the probability of occurrence of IPS

Highly destructive flood disasters have occurred frequently recently. Related to this, accurate mapping of flood areas is a necessary undertaking that helps to understand the temporal and spatial evolution patterns of floods. Thus, this paper proposes a novel, unsupervised multi-scale machine learning (ML) approach for urban flood mapping with SAR images from the perspective of information mining and