Crop mapping is essential for agricultural management and food production monitoring, but challenges like limited crop labels and poor model generalization significantly hinder large-scale crop mapping. Here, we introduce a novel Contrastive Self-Training Network (CSTN), integrating a self-training strategy and contrastive domain adaptation (CDA) for cross-region crop mapping. CSTN uses pseudo-labels

Change detection is a fundamental yet challenging task in remote sensing, crucial for monitoring urban expansion, land use changes, and environmental dynamics. However, compared with common color images, objects in remote sensing images exhibit minimal interclass variation and significant intraclass variation in the spectral dimension, with obvious scale inconsistency in the spatial dimension. Change

Changes in urban land cover (ULC) provide critical evidence of urbanization including both urban expansion and internal structural renewal. Existing global urbanization research focused on urban expansion and neglected the dynamic ULC changes occurring inside urban areas. This study addresses this issue by developing a Global Annual Urban Land Cover Fraction (GAULCF) dataset, which encompasses six

Manifold learning is an important technique for dimensionality reduction in hyperspectral images. It maps data from high dimensions to low dimensions to eliminate redundant information. However, the existing manifold learning methods cannot effectively solve the problem of lacking label information and ignore the negative impact of dimensionality reduction on sample division. To address these, we propose

Encoding geospatial location is a fundamental problem for geospatial artificial intelligence (GeoAI) research. In recent years, some methods (such as Place2Vec, Space2Vec, and Sphere2Vec) were proposed to encode geospatial point as a high-dimensional vector. However, all these geospatial location encoders were designed to generate a real number vector. So, when applied to some of the brain-inspired

At the end of the last century, the expansion of agricultural land in the arid and semi-arid regions of northern China intensified the conflict between agricultural development and ecological protection. Accurately mapping abandoned cropland is crucial for balancing these competing interests. This research evaluates the effectiveness of an innovative remote sensing method for producing 30-meter-resolution

Tidal flats represent one of the Earth’s most critical ecosystems characterized by substantial ecological value, but these areas are also among the most fragile ecosystems. A detailed topography survey of tidal flat is essential for exploring how tidal flat ecosystems respond to environmental changes and for predicting morphological shifts, thereby impacting the protection and restoration of mangrove

Small reservoirs are increasingly common across Africa. They provide decentralised access to water and support farmer-led irrigation, in addition to contributing towards mitigating the impacts of climate change. Water quality monitoring is essential to ensure the safe use of water and to understand the impact of the environment and land use on water quality. However, water quality in small reservoirs

Swidden agriculture, widely practiced by impoverished ethnic groups, continues to undergo rapid transition and transformation in tropical highlands. Exploring universal approaches for accurate mapping of newly-opened swiddens and fallows of different ages has not yet been stopped. The development of data-, information-, and knowledge-based algorithms for monitoring swidden agriculture requires integration

The learning-based integration of optical and synthetic aperture radar (SAR) satellite imagery is known to be effective in promoting the accuracy of wetland land-cover classification. However, the distribution of wetland categories is characterized as spatially heterogeneous and highly dynamic. It remains a challenge to fuse the inherent characteristics of optical and SAR data by exploiting their discriminative

Golf courses, while primarily serving as recreational spaces for high-income populations, occupy significant land areas and thus require precise spatial mapping to support land use planning and environmental management. Traditionally, it has been prohibitively expensive to accurately measure their built-up areas. This paper presents DeepGolf, an advanced framework that integrates geographic information

Urban shrinkage has become a critical global issue, influencing the sustainable development of cities across social, economic, and environmental dimensions. In Japan, which is characterized by an aging population and low birth rate, this phenomenon has now extended to metropolitan areas, presenting new challenges for urban sustainability. Although many studies have been conducted regarding urban decline

Understanding dynamics of urban land-use is crucial for comprehending urban spaces and evaluating planning strategies. A range of data-driven models based on the representation learning of multiple data sources have focused on extracting spatially explicit characteristics at the feature level for urban function inference. However, they commonly pay no attention to the systematic relationships between

Influenced by lockdown policies and anomalies in human activities, emergencies such as pandemic significantly altered the urban thermal environment. However, the spatiotemporal heat anomaly changes across and within cities during emergencies and their drivers have not been fully investigated. This study quantified the changes in the urban thermal environment in China before and during the COVID-19

Extracting and analyzing water resources in Synthetic Aperture Radar (SAR) images is crucial for flood management and environmental resource planning due to the ability to monitor ground all-weather and all-time. However, extracting water entirely from high-resolution SAR images in diverse scenarios is challenging due to variable water shapes, many low-intensity land covers similar to water, and scarce

Global warming has accelerated glacier retreat, subsequently leading to the formation of glacial lakes in high-altitude mountainous regions. These lakes represent emerging ecological water systems and could potentially pose significant hazards. Observations of these systems are constrained by their remote locations and the lack of cost-effective monitoring methods, resulting in limited understanding

Drought-induced tree mortality has increasingly expanded worldwide under the influence of climate warming, with China’s Loess Plateau (CLP) emerging as a critical hotspot for such impacts. As one of the most active tree-planting regions globally, the CLP primarily aims to achieve soil and water conservation despite facing challenges such as limited rainfall and frequent extreme drought events. However

Mapping urban top of canopy height (UTCH) is essential for quantifying urban vegetation carbon storage and developing effective vegetation management strategies. However, the scarcity and uneven distribution of urban measurement samples pose significant challenges to accurately estimating UTCH on a large scale in complex urban environments. To address this issue, this study utilized ICESat-2 photon

Interferometric Synthetic Aperture Radar (InSAR) techniques are commonly used approach for identifying Slow-moving Landslide (SML). However, most SML boundary identification with deep learning are based on single-source InSAR data, which cannot fully explore the dynamic process of destabilization, and are inefficient due to high model complexity. Meanwhile, research on automatic procession with multi-source

Multibeam sounding is a high-precision remote sensing method for seabed detection. Seabed topography classification is crucial for marine science research, resource exploration and engineering. When using multibeam data for seabed topography automatic classification, the fuzzy boundaries of different topographic entities, redundancy of multimodal data, and the lack of geological knowledge guidance

Dividing a forest point cloud dataset into tiles is a common practice in point cloud processing (e.g., individual tree segmentation), aimed at addressing memory constraints and optimizing processing efficiency. Existing methods typically utilize automatic regular clipping (e.g., rectangular clipping), which tends to result in splitting tree crowns along the cutting lines. To preserve the completeness

The Yellow River (YR), the fifth largest river in the world, plays a crucial role in regional development, making water quality assessment essential. Remote sensing provides a rapid and convenient means of observation, but water quality inversion models are often limited by the complex optical properties of inland waters and the availability of limited in-situ samples. The Forel-Ule color index (FUI)

Surface air temperature (SAT) is an essential climate variable (ECV). Models based on remote sensing data allow us to study SAT, without the need for a large network of meteorological stations. Therefore, it allows monitoring the climate in remote and extensive areas. Niclos et al. (2014) proposed parametric equations for the SAT retrieval over the Spanish Mediterranean basins. In this study, we evaluated

Satellite burned area, active fire and fire radiative power (FRP), are key to quantifying fire activity and are one of 54 essential climate variables (ECV) and it is important to validate these data to ensure their consistency. This study investigates some of the factors that influence FRP retrieval and uses Meteosat Spinning Enhanced Visible and InfraRed Imager (SEVIRI) data to do so. Analysis of

Three-dimensional (3-D) observations are crucial for understanding structures and evolution of ocean dynamic processes. This study proposes a dual U–Vision–Transformer (DUViT) model to reconstruct 3-D, eddy-resolving ocean temperature, salinity, and horizontal current fields from multi-resolution data observed by satellites at the sea surface. Daily parameter profiles from a reanalysis product with

This paper introduces IceEB, i.e., an innovative ensemble-based method that is designed to automate mapping of river ice types using radar imagery. Its goal is the merger of outcomes from three classifiers (IceMAP-R, RIACT, and IceBC) through ensemble-estimation, resulting in a highly performant and fully automated river ice-type map, which is applicable under all meteorological conditions. The first

Wetlandscapes—hydrologically connected networks of wetlands—vary over time, causing changes in their provision of hydrological, biogeochemical, and ecological functions to landscapes. Here, we developed a method for mapping wetlands and extracting wetlandscape properties from Landsat-derived inundation data and applied this method to the Lake Winnipeg Watershed (LWW). We first mapped the annual (1984–2020)

Geospatial Artificial Intelligence (GeoAI), as the integration of geospatial studies and AI, has become one of the fastest-developing research directions in spatial data science and geography. This rapid change in the field calls for a deeper understanding of the recent developments and envision where the field is going in the near future. In this work, we provide a quantitative analysis of the GeoAI

Frequent algal blooms pose a serious threat to the marine ecosystem of the East China Sea. The Geostationary Ocean Color Imager-Ⅱ (GOCI-Ⅱ), a second-generation geostationary satellite sensor, is crucial for monitoring marine environmental dynamics. To evaluate the potential of GOCI-II for identifying and monitoring the diurnal variation of algal blooms in the East China Sea, we combined a coupled ocean–atmosphere

Underwater hyperspectral target detection (HTD) technology holds pivotal value in enhancing maritime military power. However, the absorption and scattering properties of the water bodies result in the inevitable issue of mixed pixels in underwater hyperspectral images (HSIs). To address the issue, a joint hyperspectral unmixing and target detection network for underwater HSI is proposed, denoted as

The prevalent catalog-based Landslide Susceptibility Modelling (LSM) operates under the assumption that future landslide occurrences mirror past and current patterns. Due to growing urban expansion and climate change, certain landslides follow new patterns of occurrence, disrupting the foundational assumption of catalog-based LSM and leading to constraints in the effectiveness of traditional susceptibility

The application value of orthographic projection images is substantial, especially in the field of remote sensing for True Digital Orthophoto Map (TDOM) generation. Existing methods for orthographic projection image generation primarily involve geometric correction or explicit projection of photogrammetric mesh models. However, the former suffers from projection differences and stitching lines, while

The decrease in global oceanic dissolved oxygen (DO) has exerted a profound impact on marine ecosystems and biogeochemical processes. However, our comprehension of DO distribution and its global change patterns remains hindered by sparse measurements and coarse-resolution simulations. Here we presented Oxyformer, a deep learning method that accurately learns DO-related information and estimates high-resolution

Farmland extraction has been a pivotal research challenge for decades in remote sensing. Breakthrough progress has been made by relevant studies due to the advanced deep learning-based techniques. However, existing methods still pay little attention to the simultaneous instance-level farmland extraction and semantic-based 3D attribute analysis, which are essential for enabling more various agricultural

The Mountain Excavation and City Construction project (MECC) in Yan’an New District (YND) on the Chinese Loess Plateau is one of the largest geotechnical works globally. Ground deformation resulting from these extensive earthworks continues to evolve spatially and temporally even after construction is completed. Monitoring this deformation is crucial for understanding uneven post-construction subsidence

Unsupervised domain adaptation (UDA) has made great progress in cross-scene hyperspectral image (HSI) classification. Existing methods focus on aligning the distribution of source domain (SD) and target domain (TD). However, they all ignore the implicit class distribution information of TD data, which can help the model predict the class with a higher posterior probability. To solve the above issue

Deep learning (DL) based methods have recently made remarkable progress in remote sensing (RS) soil moisture (SM) retrieval applications. However, their purely “black box” algorithms suffer from a lack of interpretability, whereas methods based solely on physical mechanisms often underperform in complex scenarios. In this study, we attempt to use an SM downscaling approach that integrates thermal inertia

Due to the concealment and randomness of solid waste disposal sites, the time and manpower costs for manual on-site inspections or remote sensing visual interpretation are significantly increased. Therefore, there is an urgent demand for the development of instance segmentation models for solid waste based on remote sensing imagery. However, there is currently no instance segmentation model specifically

Managing and controlling costly natural hazards such as floods has been a fundamental and essential issue for decision-makers and planners from the past to the present. Artificial intelligence (AI) has recently proven promising to improve disaster management. There is growing interest in using AI to predict and identify flood-prone areas. However, creating accurate flood susceptibility maps with AI

Wildfire management and response requires frequent and accurate burned area mapping. How to map daily burned areas with satisfactory accuracy remains challenging due to missed detections caused by accumulating active fire points as well as the low temporal resolution of sensors onboard satellites like Sentinel-2/Landsat-8/9 and monthly burned area product generated from the Visible Infrared Imaging

As internationalization and urban competition intensify, the rise in urban population size is anticipated to bring substantial benefits to cities. To better regulate population size, researchers need accurate data and predictive modeling. This paper introduced using high-resolution population density heat maps and city maps of Hong Kong as training datasets for machine learning models. We developed

Amidst the intensifying global climate change, the increasing frequency of extreme precipitation events poses significant challenges to natural ecosystems and human societies. Particularly within the context of rapid urbanization, the mechanisms underlying the influence of urban expansion on natural disasters such as landslides have emerged as a critical area of scientific inquiry requiring further

Pandemic outbreaks significantly disrupt human work activity, which is a crucial aspect of urban daily life, potentially causing reduced income or unemployment. These disruptions often vary across different population groups and regions. However, most existing studies focus on general human mobility patterns with limited attention specific to work activity, and conduct separate analyses on population

Climate change poses significant challenges to food security, especially in semi-arid agriculture areas. Effective monitoring of crop yield is important for establishing food emergency responses and developing long-term sustainable strategies. In Morocco, where cereals are the predominant crops, yield forecasting is important for addressing the yield gap as it enables farmers to take preventive actions

Curtailing encroachment is dependent on effectively identifying where problematic species occur. However, traditional classification methods struggle to distinguish spectrally similar species. New techniques that incorporate environmental variables (edaphic, climatic, and topographic characteristics) into classification can refine predictions and help identify important factors associated with species

Hyperspectral anomaly detection (HAD) holds significant importance in remote sensing image processing and has been recently empowered by deep learning-based methods. Despite achieving good performance by reconstructing the background and suppressing abnormal targets, these methods often fail to distinguish the attributes of latent features during background reconstruction, resulting in unsatisfactory

Effective identification and control of malaria vector larval breeding habitats are crucial for the management and eradication of malaria. Despite its importance, the last decade has seen a decline in data availability and intervention efforts due to reduced attention and prioritization. This study addresses the geographic data scarcity concerning Anopheles funestus larval breeding habitats in a malaria-prone

In situations with a limited number of posed images, choosing the most suitable viewpoints becomes crucial for accurate Neural Radiance Fields (NeRF) modeling. Current approaches for view selection often rely on heuristic methods or are computationally intensive. To address these challenges, we introduce a new framework, OptiViewNeRF, which leverages scene uncertainty to guide the view selection process

Water-based photovoltaics (WPV) have emerged as a promising solution to land-use conflicts associated with solar photovoltaic systems. Accurate monitoring of the spatiotemporal distribution of WPV is essential for evaluating its development potential, environmental impacts, and informing policy decisions. Satellite remote sensing data offer a feasible approach for WPV mapping and monitoring. However

Accurate evapotranspiration (ET) estimation is crucial for optimizing irrigation and managing water resources at the field scale. This study investigates the potential of unmanned aerial vehicles (UAVs) equipped with the MicaSense Altum sensor for high-resolution ET mapping using the Hybrid Single Source Energy Balance (HSEB) model. We focused on a 4.5 ha sprinkle-irrigated potato field at the American

Building extraction from very high-resolution remote-sensing images still faces two main issues: (1) small buildings are severely omitted and the extracted building shapes have a low consistency with ground truths. (2) supervised deep-learning methods have poor performance in few-shot scenarios, limiting the practical application of these methods. To address the first issue, we propose an asymmetric

As global urbanization intensifies, conflicts between humans and urban infrastructure increasingly affect socio-economic and environmental sustainability. Recently, using image data and deep learning to investigate the interactions between humans and urban infrastructure has been a popular approach since the fast development of Artificial Intelligence (AI). However, the convergence of data fusion,

Urban area-of-interest (AOI) refers to an integrated urban functional zone with defined polygonal boundaries. The rapid development of urban commerce has led to increasing demands for highly accurate and timely AOI data. However, existing research primarily focuses on coarse-grained functional zones for urban planning or regional economic analysis, and often neglects AOI’s expiration in the real world

Accurately mapping urban functional zone (UFZ) provides crucial foundational geographic information services for urban sustainable development, territorial spatial planning, and public resource allocation. UFZs are blocks within urban environments that serve specific functions, typically comprising physical objects with specific spatial distribution patterns and semantic objects of various types. However

The importance of water resources in supporting food production is ever increasing, especially in the face of climate change, urbanization and population growth. This study aims to map and analyze the spatio-temporal dynamics of irrigated agricultural areas to support improved planning of irrigation water and irrigation sector in Nepal. Using the Normalized Difference Vegetation Index (NDVI) from the

This paper introduces a method for estimating forest above-ground biomass (AGB) using the Interferometric SAR (InSAR)-based Phase Histogram (PH) technique. This novel technique allows for the extraction of 3D vertical forest structure using only a single interferometric pair to acquire a coarse-resolution backscatter intensity distribution in the height direction. Through 3D backscatter distribution

Multi-temporal synthetic aperture radar interferometry (MT-InSAR) offers unique advantages in monitoring ground deformation and structural stability along the metro lines. However, a vast number of complex deformation points, millions and even more, can be derived from InSAR making it challenging to identify the deformation hotspot in time series automatically. This paper proposes a novel method for

Under the background of global aging, outdoor public spaces play a crucial role in promoting physical activity among older individuals, helping to advance the process of healthy aging. Among these spaces, urban greenways are particularly effective in encouraging walking behaviors and improving both physical and mental health. However, current assessments of greenway’s walkability have mainly focused

Although real-time semantic segmentation of pavement cracks is crucial for road evaluation and maintenance decision-making, it is a challenging task due to low operational efficiency and over-segmentation of existing methods. To address these challenges, in this paper, incorporating Transformers and CNNs, we propose a real-time triple-branch crack semantic segmentation network (RTCNet) using digital

Irrigation is a critical component of global water usage, accounting for approximately 70 % of water consumption. As the world’s population continues to grow, the demand for food will increase, making it essential to improve irrigation management by reducing water waste and increasing efficiency. This study aims to develop and validate a fuzzy decision-making system that determines crop irrigation