Strategic forest inventories are required to support management, enable monitoring, and inform policy development. These inventories are typically developed using photointerpretation of aerial imagery, followed by manual stand delineation and attribution. The process of delineating and attributing these segments can be costly, time consuming, and subjective. Many spectrally driven segmentation algorithms

Nonlinear Bκ-embeddings (NLE) –recently introduced for the analysis of complex systems and complex spatiotemporal behavior– have been applied here to develop a reliable land degradation assessment and monitoring tool. Normalized Difference Vegetation Index (NDVI) time series, which are non-stationary and present seasonal, long-term and short-term variations, can be nonlinearly embedded into an encompassing

Biological soil crusts, commonly referred to as biocrusts (including lichens), play a vital ecological role in arid environments by stabilizing the soil, reducing erosion, and influencing nutrient and water cycles. Remote sensing has become an indispensable tool for mapping biocrusts because it can efficiently analyze extensive and inaccessible areas. Furthermore, spectral analysis of biocrusts provides

The goal of referring remote sensing image segmentation (RRSIS) is to extract specific pixel-level regions within an aerial image via a natural language expression. Recent advancements, particularly Transformer-based fusion designs, have demonstrated remarkable progress in this domain. However, existing methods primarily focus on refining visual features using language-aware guidance during the cross-modal

Fine-grained economic mapping through urban representation learning has emerged as a crucial tool for evidence-based economic decisions. While existing methods primarily rely on supervised or unsupervised approaches, they often overlook semi-supervised learning in data-scarce scenarios and lack unified multi-task frameworks for comprehensive sectoral economic analysis. To address these gaps, we propose

Building information extraction holds significant application value in smart city development, urban planning, and management. With the accelerating process of urbanization, mid- and high-rise buildings are increasingly prevalent. In orthophotos, the roofs of tall buildings often do not fully overlap with their footprints. In satellite images from oblique angles, buildings may also be obstructed or

Accurate recognition and vectorization of agricultural cultivation field parcels (CFP) are crucial for agricultural monitoring. However, the diverse sizes and shapes of parcels, inherent blurriness of boundaries, and adhesion of densely distributed parcels pose considerable challenges in extracting complete and separable parcels from high-resolution imagery. To address these issues, we propose an end-to-end

Multi-baseline (MB) phase unwrapping (PU) represents the core processing step of MB InSAR, which overcomes discontinuous terrain height estimation. However, MBPU faces the challenge of low noise robustness. A new robust cutting plane–pure integer programming (CP-PIP) PU algorithm that considers the fringe frequency for dual-baseline InSAR is proposed to address this problem. First, we establish a closed-region

Hyperspectral target detection (HTD) aims to identify specific materials based on spectral information in hyperspectral imagery and can detect extremely small-sized objects, some of which occupy a smaller than one-pixel area. However, existing HTD methods are developed based on per-pixel binary classification, neglecting the three-dimensional cube structure of hyperspectral images (HSIs) that integrates

National forest inventory (NFI) data has become an indispensable reference for model training and validation when estimating forest aboveground biomass (AGB) using satellite observations. However, obtaining statistically sufficient NFI data for model training is challenging for countries with vast land areas and extensive forest coverage like Canada. This study aims to directly upscale all available

Accurate, spatially explicit quantification of vegetation structure in drylands can improve our understanding of the important role that these critical ecosystems play in the Earth system. In semiarid woodland settings, remote sensing of vegetation structure is challenging due to low tree height, cover, and greenness as well as limited spatial and temporal availability of airborne lidar data. These

Hyperspectral (HSP) video data can offer rich spectral-spatial–temporal information crucial for capturing object dynamics, attenuating the drawbacks of classical unimodal and multi-modal tracking. Current HSP tracking arts often suffer from feature refinements and information interactions, sealing the ceiling of capabilities. This study presents ProFiT, an innovative prompt-guided frequency-aware filtering

Abundant, well-annotated multimodal data in remote sensing are pivotal for aligning complex visual remote sensing (RS) scenes with human language, enabling the development of specialized vision language models across diverse RS interpretation tasks. However, annotating RS images with rich linguistic semantics at scale demands expertise in RS and substantial human labor, making it costly and often impractical

In spring, many deciduous broadleaf trees start with flower emergence and then leaf emergence, which are two key phenological events, as they signal the onset of reproduction and vegetative growth in a year. These trees provide essential resources for early pollinators searching for flowers, contribute to biodiversity, and create socio-economic benefits through tourism. Accurate detection and monitoring

Optical remote sensing images are crucial data sources for various applications, including agricultural monitoring, land cover classification, and urban planning. However, cloud cover often hinders their effectiveness, which poses a significant challenge to downstream tasks. To address this issue, we introduce the Similarity-based Multimodal De-Clouding Network (SMDCNet), an innovative framework that

Real-time processing of UAV-based agricultural remote sensing images is essential for precision agriculture, especially for low-overlap, large-format image sequence. These images, characterized by large formats, repetitive textures, and low overlap, pose significant challenges for real-time UAV photogrammetry. To address these challenges, this paper proposes a novel real-time matching and pose reconstruction

The coastal environments of the Black Sea are of high ecological and socio-economic importance. Understanding changes along this extensive and complex coastline can help us comprehend the pressures from nature, society, and extreme events, providing valuable insights for more effective management and the prevention of future adverse changes. Current methods for monitoring coastal dynamics rely on the

Accurate and complete reconstruction of the high-quality three-dimensional (3D) geometric information from stereo images has been one of the challenging tasks in the fields of photogrammetry and computer vision. Although tremendous efforts have been made in stereo matching by utilizing convolutional neural network (CNN)-based deep learning architectures, the matching ambiguities in the case of occlusions

Surface water resources are widely distributed and undergo rapid fluctuations, necessitating large-scale, high-frequency monitoring. Remote sensing technologies provide critical data for this purpose, but challenges such as data gaps and contamination hinder the effective observation of surface water dynamics at fine temporal scales. This limitation can obscure the recording of short-term water variations

Oblique photogrammetry using unmanned aerial vehicles (UAV) is crucial to 3D scene reconstruction. Nevertheless, oblique images are generally acquired with large overlaps, and sometimes with multiple views. Although this increases the level of data completeness, it introduces additional and sometimes redundant computations in tie-point matching, creating overly dense camera connections in the pose

Unmanned aerial vehicle remote sensing technology has become a key technology in crop breeding, which can achieve high-throughput and non-destructive collection of crop phenotyping data. However, the multidisciplinary nature of breeding has brought technical barriers and efficiency challenges to knowledge mining. Therefore, it is important to develop a smart breeding goal tool to mine cross-domain

Generating fine-resolution land cover maps from coarse-resolution multi-spectral images is an effective solution for addressing the mixed pixel problem inherent in remote sensing. Traditional subpixel mapping strategies, encompassing spectral unmixing followed by super-resolution mapping and image super-resolution followed by classification, often have limitations related to inadequate handling of

Accurate detection of underground natural gas micro-leakage is essential for operational safety. However, existing spectral-spatial models based on hyperspectral imagery (HSI) face challenges of limited integration with crop physiology and low interpretability. To address this, we propose LUEGNet, a novel framework that integrates the SAFY-NG crop growth model with deep learning through light use efficiency

The advances in 3D reconstruction technology, such as photogrammetry and LiDAR scanning, have made it easier to reconstruct accurate and detailed 3D models for urban scenes. Nevertheless, these reconstructed models often contain a large number of geometry primitives, making interactive manipulation and rendering challenging, especially on resource-constrained devices like virtual reality platforms

Compared to remote sensing image (RSI) segmentation methods that rely solely on label-based learning, vision-language models (VLMs) can leverage language guidance to model the inherent relationships between farmland and its surrounding environment. However, unlike natural images, where the objects are typically singular and concentrated, farmland remote sensing images often contain a variety of complex

The accurate retrieval of chlorophyll-a (Chla) concentrations in large-scale and highly-dynamic aquatic environments is challenging because of the complex and variable optical water properties, necessitating regionally-tuned or water-type-specific algorithms. In this study, we developed and validated an integrated algorithm to estimate Chla in optically diverse waters using the second-generation Coastal

When large floods occur, satellite data are useful for providing emergency managers with frequent and synoptic maps of affected areas, even on a daily basis. Only the use of on-demand SAR data enables the high-resolution monitoring of flood events through acquisitions performed day and night, and regardless of cloud cover, over different areas. However, continuous flood mapping generally requires combining

Accurate, detailed, and high-frequent bathymetry is crucial for shallow seabed areas facing intense climatological and anthropogenic pressures. Current methods utilizing airborne or satellite optical imagery to derive bathymetry primarily rely on either Structure-from-Motion and Multi-View Stereo (SfM-MVS) with refraction correction or Spectrally Derived Bathymetry (SDB). However, SDB methods often

In recent decades, severe environmental pollution and resource scarcity have made it increasingly urgent to monitor the distribution and changes of surface geological environmental elements. As the main components of geological environmental elements, different types of lithology are characterized by their diversity, fragmented distribution, and ambiguous boundaries. Remote sensing images have further

High-resolution Mars digital terrain models (DTMs) are crucial for exploration missions and scientific research. However, traditional photogrammetry is constrained by stereo image coverage and configuration, while laser altimetry suffers from sparse laser footprint distribution, leading to limited terrain accuracy. While the recent photoclinometric and deep learning methods can generate promising 3D

Vision-based localization techniques are effective UAV localization solutions for GNSS-denied conditions, however they depend on costly, complex, and seasonally variable satellite images or 3D maps, whereas humans can determine location using vector maps. Inspired by human navigation strategies, we propose VecMapLocNet , which uses vector maps to determine UAV 3-DoF poses (latitude, longitude, and

Mars is rich in color information, and color images can more accurately represent the surface morphology of Mars, aiding in the interpretation of its geomorphological and geological characteristics. Obtaining complete, consistent, well-quantified, and intuitive colors of Mars from remote sensing is challenging due to the instrument limitations, varying the illumination and atmospheric conditions, the

The lack of large-scale, high-quality training data remains a significant barrier in modern change detection studies. Existing datasets for change detection are typically limited in volume, suffer from category imbalance, and exhibit low label quality, thus impeding the development of data-driven deep learning methods and their practical applications. Artificial intelligence-generated content (AIGC)

Variations of crop phenology are critical indicators of growth conditions and are essential for scheduling irrigation and fertilization activities to mitigate climate risks. Accurately characterizing carry-over climate impacts and phenological associations, especially the delayed influence of earlier stages development on later stages, is key to understanding crop phenological dynamics under changing

Quantification of anthropogenic methane emissions from coal mines and oil & gas facilities is essential to global methane accounting and management. Matched Filter (MF), a classic method for point-source methane emission detection, was proven less effective over heterogeneous land covers given substantial false positives of methane enhancement retrievals due to large uncertainties in background spectrum

The accurate identification of land-surface changed classes when locating changed areas with regular boundaries from satellite images represents a significant challenge, particularly in these areas with considerable spectra and seasonal differences. This paper develops a boundary-guided Siamese multitask network, namely BGSNet, for the purpose of semantic change detection (SCD) from high-resolution

Covers and surface materials composition of urban, peri-urban and rural landscapes is significant information for environmental, climate and human-ecosystems interaction monitoring and modeling, as well as for addressing specific urban planning and improving environmental management. In this study the multiple endmember spectral mixture analysis (MESMA) was exploited to overcome the low spatial resolution

Synthetic Aperture Radar (SAR) is an active microwave imaging and earth observation device capable of penetrating through clouds, rain, and fog, enabling it to operate effectively regardless of the weather conditions and throughout the day. However, speckle noise in SAR images can make them difficult to interpret, and optical images are often difficult to observe when they are covered by clouds. Therefore

Deep learning methods have demonstrated significant success in estimating land surface parameters from satellite data. However, these methods often require large sample sizes for optimal performance, which can be difficult to obtain. This study introduces a novel framework that combines transfer learning (TL) and data augmentation (DA) to improve the performance of a deep learning model, the residual

Spatially explicit information on crop distribution over large areas and long timespans is essential for optimizing agricultural spatial allocation and promoting food security. Despite the emergence of numerous remote sensing-based approaches for crop type mapping in recent years, the generation of long-term and high-quality crop type maps still remains challenging due to the poor spatiotemporal scalability

Evaluating uncertainties and errors in interferometric synthetic aperture radar (InSAR)-derived displacements is challenging but important for any interpretation or analysis. InSAR observation error mainly arises from tropospheric delays, particularly tropospheric turbulence which is hard to quantify in a deterministic way (e.g., using global atmospheric models). Here we propose a new variance–covariance

Thermal infrared remote sensors provide cost-effective and widespread land surface temperatures (LSTs) but often with spatiotemporal gaps due to discrete sampling and synoptic disturbance, greatly limiting their reliability and application. Current gap-filling methods have been primarily developed and validated for medium- to low-resolution LSTs; however, with rising demand for spatiotemporally continuous

Deep learning demonstrates significant potential in enhancing the techniques of remote sensing image (RSI) matching. The current review delves into the incorporation of deep learning in RSI matching methods. Four predominant strategies are elucidated: area-based matching, feature-based matching, regression-based matching, and unsupervised learning methods. Area-based strategies concentrate on the quantification

Tidal flats are crucial components of coastal geomorphic systems, where the ocean meets the land. Timely and accurate topographic maps of tidal flats are essential for sustainable coastal management and development. Although satellite imagery-based inversion methods offer a cost-effective solution for constructing large-scale intertidal topography, their accuracy remains heavily dependent on the availability

Formation-level vegetation mapping is pivotal for understanding ecological processes and mechanisms, as it reveals the distribution of dominant species that shape ecosystem structure and dynamics. However, fast and accurate formation-level mapping over large geographic areas is often hindered by the lack of robust mapping frameworks, limited field survey data, and unscientific or inefficient division

The sparse and uneven distribution of ground-based air quality monitoring stations poses significant challenges for large scale PM2.5 pollution mapping. Spatially heterogenous land–atmosphere interactions often lead to large uncertainties in satellite-based PM2.5 estimations from global modeling strategies. To enhance global PM2.5 mapping accuracy, particularly in poorly monitored regions, we propose

The development of in situ observations has significantly improved ocean heat content (OHC) estimation. However, high-resolution OHC data remain limited, hindering detailed studies on mesoscale oceanic warming variability. This study used a deep learning method-Densely Deep Neural Network (DDNN) to reconstruct a high-resolution (0.25° × 0.25°) global OHC dataset for the upper 2000m ocean from 1993

Land surface temperature (LST) is a fundamental parameter in environmental and climatic studies. Over the past decades, various clear-sky LST retrieval methods have been developed, among which the temperature-emissivity separation (TES) algorithm prevails due to its good accuracy and the simultaneous retrieval of LST and land surface emissivity (LSE). However, TES relies on complete atmospheric profiles

Recent advancements in change detection (CD) using fully-supervised methods have been significant; however, effectively applying CD in scenarios where labels are unavailable remains a challenge. To address this, our study introduces a new task, domain adaptive change detection (DACD), which transfers change knowledge from a labeled CD dataset (source domain) to an unlabeled CD dataset (target domain)

Information on the rice agricultural system, including not only planting area but also phenology and cropping intensity, is critical for advancing our understanding of food and water security, methane emissions, carbon and nitrogen cycles, and avian influenza transmission. However, existing efforts primarily focus on mapping planting area and lack a comprehensive picture of the rice agricultural system

Complex cropping patterns with crop diversity are an underexploited treasure for global food security. However, significant methodological and dataset gaps in fully characterizing cropland cultivated with multiple crops and rotation sequences hinder our ability to understand and promote sustainable agricultural systems. Existing crop mapping models are challenged by the deficiency of ground reference

Lunar surface mineralogy plays a crucial role in characterizing the distribution and abundance of silicate minerals, providing pivotal insights into the geological evolution of the Moon. Existing lunar mineral distribution maps are primarily derived from the calibration of Apollo and Luna sample datasets, which are predominantly older than 3.0 Ga. However, these maps lack critical constraints from

Systematic decametric resolution global mapping of vegetation biophysical variables, including fraction of absorbed photosynthetically active radiation (fAPAR), fraction of vegetation cover (fCOVER), and leaf area index (LAI), is required to support various activities, including climate adaptation, crop management, biodiversity monitoring, and ecosystem assessments. The Canada Centre for Remote Sensing

Polygonal building outlines are crucial for geographic and cartographic applications. The existing approaches for outline extraction from aerial or satellite imagery are typically decomposed into subtasks, e.g., building masking and vectorization, or treat this task as a sequence-to-sequence prediction of ordered vertices. The former lacks efficiency, and the latter often generates redundant vertices

The fusion of SAR image features from multiple views can effectively improve the recognition performance of SAR ATR tasks. However, when the number of raw samples in SAR images is limited, multiple fusions of SAR image features from different views of the same class may result in significant feature redundancy, causing overfitting of the model. To solve those problems, we propose a multiview and multi-level

Wearable laser scanning (WLS) system has the advantages of flexibility and portability. It can be used for determining the user’s path within a prior map, which is a huge demand for applications in pedestrian navigation, collaborative mapping, augmented reality, and emergency rescue. However, existing LiDAR-based global localization methods suffer from insufficient robustness, especially in complex

Existing crop type maps usually rely on extensive ground truth, limiting the potential applicability in regions without any crop labels. Unsupervised clustering offers a promising approach for crop mapping in regions lacking labeled crop samples. However, due to the high-dimensional complexity and pronounced temporal dependencies of crop time series, existing unsupervised clustering methods are inadequate

Individual Tree Detection (ITD) can automatically recognize single trees and generate large-scale individual tree maps, providing essential insights for tree-by-tree forest management. However, ITD using remote sensing data becomes increasingly challenging as data quality and spatial resolution decrease. This paper proposes a Super-Resolution (SR)-based ITD method to predict individual tree locations

Accurate and efficient estimation of crop yield is crucial for enhancing crop variety testing, optimizing cultivation practices, and supporting effective crop management to ensure sustainable production. However, remote sensing-based yield models often face limitations due to geographical variability and diverse cultivation techniques, affecting both their model accuracy and transferability. This study

In the last twenty years, Structure from Motion (SfM) has been a constant research hotspot in the fields of photogrammetry, computer vision, robotics etc., whereas real-time performance has only recently emerged as a topic of growing interest. This work builds upon the original on-the-fly SfM (Zhan et al., 2024) and presents an updated version (v2) with three new advancements to get better SfM reconstruction