Search Results (74)

The Xixia Imperial Tombs (XITs) represent a crucial, yet still largely mysterious, component of the Tangut civilization’s legacy. Located in northwestern China, this extensive necropolis offers invaluable insights into the Tangut state, culture, and burial practices. This study employs an integrated approach utilizing multi-resolution and multi-temporal satellite remote sensing data, including Gaofen-2 (GF-2), Landsat-8 OLI, declassified GAMBIT imagery, and Google Earth, combined with deep learning techniques, to conduct a comprehensive archaeological investigation of the XITs’ burial landscape. We performed geomorphological analysis of the surrounding environment and automated identification and mapping of burial mounds and mausoleum features using YOLOv5, complemented by manual interpretation of very-high-resolution (VHR) satellite imagery. Spectral indices and image fusion techniques were applied to enhance the detection of archaeological features. Our findings demonstrated the efficacy of this combined methodology for archaeology prospect, providing valuable insights into the spatial layout, geomantic considerations, and preservation status of the XITs. Notably, the analysis of declassified GAMBIT imagery facilitated the identification of a suspected true location for the ninth imperial tomb (M9), a significant contribution to understanding Xixia history through remote sensing archaeology. This research provides a replicable framework for the detection and preservation of archaeological sites using readily available satellite data, underscoring the power of advanced remote sensing and machine learning in heritage studies. Full article

Despite the growing availability of very-high-resolution (VHR) remote sensing imagery, extracting fine-grained urban features and materials remains a complex task. Land use/land cover (LULC) maps generated from satellite imagery often fall short in providing the resolution needed for detailed urban studies. While hyperspectral imagery offers rich spectral information ideal for material classification, its complex acquisition process limits its use on aerial platforms such as manned aircraft and unmanned aerial vehicles (UAVs), reducing its feasibility for large-scale urban mapping. This study explores the potential of using only RGB and LiDAR data from VHR aerial imagery as an alternative for urban material classification. We introduce an end-to-end workflow that leverages a multi-head segmentation network to jointly classify roof and ground materials while also segmenting individual roof components. The workflow includes a multi-offset self-ensemble inference strategy optimized for aerial data and a post-processing step based on digital elevation models (DEMs). In addition, we present a systematic method for extracting roof parts as polygons enriched with material attributes. The study is conducted on six cities in Flanders, Belgium, covering 18 material classes—including rare categories such as green roofs, wood, and glass. The results show a 9.88% improvement in mean intersection over union (mIOU) for building and ground segmentation, and a 3.66% increase in mIOU for material segmentation compared to a baseline pyramid attention network (PAN). These findings demonstrate the potential of RGB and LiDAR data for high-resolution material segmentation in urban analysis. Full article

Remote sensing is essential for mapping and monitoring burnt areas. Integrating Very High-Resolution (VHR) data with medium-resolution datasets like Landsat and deep learning algorithms can enhance mapping accuracy. This study employs two deep learning algorithms, UNET and Gated Recurrent Unit (GRU), to classify burnt areas in the Bandipur Forest, Karnataka, India. We explore using VHR imagery with limited samples to train models on Landsat imagery for burnt area delineation. Four models were analyzed: (a) custom UNET with Landsat labels, (b) custom UNET with PlanetScope-labeled data on Landsat, (c) custom UNET-GRU with Landsat labels, and (d) custom UNET-GRU with PlanetScope-labeled data on Landsat. Custom UNET with Landsat labels achieved the best performance, excelling in precision (0.89), accuracy (0.98), and segmentation quality (Mean IOU: 0.65, Dice Coefficient: 0.78). Using PlanetScope labels resulted in slightly lower performance, but its high recall (0.87 for UNET-GRU) demonstrating its potential for identifying positive instances. In the study, we highlight the potential and limitations of integrating VHR with medium-resolution satellite data for burnt area delineation using deep learning. Full article

Maximizing the energy output of photovoltaic (PV) systems is becoming increasingly important. Consequently, numerous approaches have been developed over the past few years that utilize remote sensing data to predict or map solar potential. However, they primarily address hypothetical scenarios, and few focus on improving existing installations. This paper presents a novel method for optimizing the tilt angles of existing PV arrays by integrating Very High Resolution (VHR) satellite imagery and airborne Light Detection and Ranging (LiDAR) data. At first, semantic segmentation of VHR imagery using a deep learning model is performed in order to detect PV modules. The segmentation is refined using a Fine Optimization Module (FOM). LiDAR data are used to construct a 2.5D grid to estimate the modules’ tilt (inclination) and aspect (orientation) angles. The modules are grouped into arrays, and tilt angles are optimized using a Simulated Annealing (SA) algorithm, which maximizes simulated solar irradiance while accounting for shadowing, direct, and anisotropic diffuse irradiances. The method was validated using PV systems in Maribor, Slovenia, achieving a 0.952 F1-score for module detection (using FT-UnetFormer with SwinTransformer backbone) and an estimated electricity production error of below 6.7%. Optimization results showed potential energy gains of up to 4.9%. Full article

In recent years, a growing number of sensors that provide imagery with constantly increasing spatial resolution are being placed on the orbit. Contemporary Very-High-Resolution Satellites (VHRS) are capable of recording images with a spatial resolution of less than 0.30 m. However, until now, these scenes were acquired in a static way. The new technique of the dynamic acquisition of video satellite imagery has been available only for a few years. It has multiple applications related to remote sensing. However, in spite of the offered possibility to detect dynamic targets, its main limitation is the degradation of the spatial resolution of the image that results from imaging in video mode, along with a significant influence of lossy compression. This article presents a methodology that employs Generative Adversarial Networks (GAN). For this purpose, a modified ESRGAN architecture is used for the spatial resolution enhancement of video satellite images. In this solution, the GAN network generator was extended by the Uformer model, which is responsible for a significant improvement in the quality of the estimated SR images. This enhances the possibilities to recognize and detect objects significantly. The discussed solution was tested on the Jilin-1 dataset and it presents the best results for both the global and local assessment of the image (the mean values of the SSIM and PSNR parameters for the test data were, respectively, 0.98 and 38.32 dB). Additionally, the proposed solution, in spite of the fact that it employs artificial neural networks, does not require a high computational capacity, which means it can be implemented in workstations that are not equipped with graphic processors. Full article

Change detection (CD), a crucial technique for observing ground-level changes over time, is a challenging research area in the remote sensing field. Deep learning methods for CD have made significant progress in remote sensing intelligent interpretation. However, with very high-resolution (VHR) satellite imagery, technical challenges such as insufficient mining of shallow-level features, complex transmission of deep-level features, and difficulties in identifying change information features have led to severe fragmentation and low completeness issues of CD targets. To reduce costs and enhance efficiency in monitoring tasks such as changes in national resources, it is crucial to promote the practical implementation of automatic change detection technology. Therefore, we propose a deep learning approach utilizing heterogeneity enhancement and homogeneity restraint for CD. In addition to comprehensively extracting multilevel features from multitemporal images, we introduce a cosine similarity-based module and a module for progressive fusion enhancement of multilevel features to enhance deep feature extraction and the change information utilization within feature associations. This ensures that the change target completeness and the independence between change targets can be further improved. Comparative experiments with six CD models on two benchmark datasets demonstrate that the proposed approach outperforms conventional CD models in various metrics, including recall (0.6868, 0.6756), precision (0.7050, 0.7570), F1 score (0.6958, 0.7140), and MIoU (0.7013, 0.7000), on the SECOND and the HRSCD datasets, respectively. According to the core principles of change detection, the proposed deep learning network effectively enhances the completeness of target vectors and the separation of individual targets in change detection with VHR remote sensing images, which has significant research and practical value. Full article

The spatial and spectral information brought by the Very High Resolution (VHR) and multispectral satellite images present an advantage for Satellite-Derived Bathymetry (SDB), especially in shallow-water environments with dense wave patterns. This work focuses on Tavşan Island, located in the Sea of Marmara (SoM), and aims to evaluate the accuracy and reliability of two machine learning (ML) regression methods, Multi-Layer Perceptron (MLP) and Random Forest (RF), for bathymetry mapping using Worldview-2 (WV-2) imagery. In situ bathymetry measurements were collected to enhance model training and validation. Pre-processing techniques, including water pixel extraction, sun-glint correction, and median filtering, were applied for image enhancement. The MLP and RF regression models were then trained using a comprehensive dataset that included spectral bands from the satellite image and corresponding ground truth depth values. The accuracy of the models was assessed using metrics such as Root-Mean-Square Error (RMSE), Mean Absolute Error (MAE), and R² value. The RF regression model outperformed the MLP model, with a maximum R² value of 0.85, lowest MAE values from 0.65 to 1.86 m, and RMSE values from 0.93 to 2.41 m at depth intervals between 6 and 9 m. These findings highlight the effectiveness of ML regression methods, specifically the RF model, for SDB based on remotely sensed images in wave-dense shallow-water environments. Full article

The OpenStreetMap (OSM) project is an open-source, community-based, user-generated street map/data service. It is the most popular project within the state of the art for crowdsourcing. Although geometrical features and tags of annotations in OSM are usually precise (particularly in metropolitan areas), there are instances where volunteer mapping is inaccurate. Despite the appeal of using OSM semantic information with remote sensing images, to train deep learning models, the crowdsourced data quality is inconsistent. High-resolution remote sensing image segmentation is a mature application in many fields, such as urban planning, updated mapping, city sensing, and others. Typically, supervised methods trained with annotated data may learn to anticipate the object location, but misclassification may occur due to noise in training data. This article combines Very High Resolution (VHR) remote sensing data with computer vision methods to deal with noisy OSM. This work deals with OSM misalignment ambiguity (positional inaccuracy) concerning satellite imagery and uses a Convolutional Neural Network (CNN) approach to detect missing buildings in OSM. We propose a translating method to align the OSM vector data with the satellite data. This strategy increases the correlation between the imagery and the building vector data to reduce the noise in OSM data. A series of experiments demonstrate that our approach plays a significant role in (1) resolving the misalignment issue, (2) instance-semantic segmentation of buildings with missing building information in OSM (never labeled or constructed in between image acquisitions), and (3) change detection mapping. The good results of precision (0.96) and recall (0.96) demonstrate the viability of high-resolution satellite imagery and OSM for building detection/change detection using a deep learning approach. Full article

Coldwater streams are crucial habitats for many biota including Salmonidae and Cottidae species that are unable to tolerate warmer water temperatures. Accurate classification of coldwater streams is essential for their conservation, restoration, and management, especially in light of increasing human disturbance and climate change. Coldwater streams receive cooler groundwater inputs and, as a result, typically remain ice-free during the winter. Based on this empirical thermal evidence, we examined the potential of very high-resolution (VHR) satellite and uncrewed aerial system (UAS) imagery to (i) detect coldwater streams using semi-automatic classification versus visual interpretation approaches, (ii) examine the physical factors that contribute to inaccuracies in detecting coldwater habitats, and (iii) use the results to identify inaccuracies in existing thermal stream classification datasets and recommend coverage updates. Due to complex site conditions, semi-automated classification was time consuming and produced low mapping accuracy, while visual interpretation produced better results. VHR imagery detected only the highest quality coldwater streams while lower quality streams that still met the thermal and biological criteria to be classified as coldwater remained undetected. Complex stream and site variables (narrow stream width, canopy cover, terrain shadow, stream covered by ice and drifting snow), image quality (spatial resolution, solar elevation angle), and environmental conditions (ambient temperature prior to image acquisition) make coldwater detection challenging; however, UAS imagery is uniquely suited for mapping very narrow streams and can bridge the gap between field data and satellite imagery. Field-collected water temperatures and stream habitat and fish community inventories may be necessary to overcome these challenges and allow validation of remote sensing results. We detected >30 km of coldwater streams that are currently misclassified as warmwater. Overall, visual interpretation of VHR imagery it is a relatively quick and inexpensive approach to detect the location and extent of coldwater stream resources and could be used to develop field monitoring programs to confirm location and extent of coldwater aquatic resources. Full article

Urban forests globally face severe degradation due to human activities and natural disasters, making deforestation an urgent environmental challenge. Remote sensing technology and very-high-resolution (VHR) bitemporal satellite imagery enable change detection (CD) for monitoring forest changes. However, deep learning techniques for forest CD concatenate bitemporal images into a single input, limiting the extraction of informative deep features from individual raw images. Furthermore, they are developed for middle to low-resolution images focused on specific forests such as the Amazon or a single element in the urban environment. Therefore, in this study, we propose deep learning-based urban forest CD along with overall changes in the urban environment by using VHR bitemporal images. Two networks are used independently: DeepLabv3+ for generating binary forest cover masks, and a deeply supervised image fusion network (DSIFN) for the generation of a binary change mask. The results are concatenated for semantic CD focusing on forest cover changes. To carry out the experiments, full scene tests were performed using the VHR bitemporal imagery of three urban cities acquired via three different satellites. The findings reveal significant changes in forest covers alongside urban environmental changes. Based on the accuracy assessment, the networks used in the proposed study achieved the highest F1-score, kappa, IoU, and accuracy values compared with those using other techniques. This study contributes to monitoring the impacts of climate change, rapid urbanization, and natural disasters on urban environments especially urban forests, as well as relations between changes in urban environment and urban forests. Full article

Sea fog detection (SFD) presents a significant challenge in the field of intelligent Earth observation, particularly in analyzing meteorological satellite imagery. Akin to various vision tasks, ImageNet pre-training is commonly used for pre-training SFD. However, in the context of multi-spectral meteorological satellite imagery, the initial step of deep learning has received limited attention. Recently, pre-training with Very High-Resolution (VHR) satellite imagery has gained increased popularity in remote-sensing vision tasks, showing the potential to replace ImageNet pre-training. However, it is worth noting that the meteorological satellite imagery applied in SFD, despite being an application of computer vision in remote sensing, differs greatly from VHR satellite imagery. To address the limitation of pre-training for SFD, this paper introduces a novel deep-learning paradigm to the meteorological domain driven by Masked Image Modeling (MIM). Our research reveals two key insights: (1) Pre-training with meteorological satellite imagery yields superior SFD performance compared to pre-training with nature imagery and VHR satellite imagery. (2) Incorporating the architectural characteristics of SFD models into a vanilla masked autoencoder (MAE) can augment the effectiveness of meteorological pre-training. To facilitate this research, we curate a pre-training dataset comprising 514,655 temporal multi-spectral meteorological satellite images, covering the Bohai Sea and Yellow Sea regions, which have the most sea fog occurrence. The longitude ranges from 115.00E to 128.75E, and the latitude ranges from 27.60N to 41.35N. Moreover, we introduce SeaMAE, a novel MAE that utilizes a Vision Transformer as the encoder and a convolutional hierarchical decoder, to learn meteorological representations. SeaMAE is pre-trained on this dataset and fine-tuned for SFD, resulting in state-of-the-art performance. For instance, using the ViT-Base as the backbone, SeaMAE pre-training which achieves $64.18\%$ surpasses from-scratch learning, natural imagery pre-training, and VRH satellite imagery pre-training by $5.53\%$, $2.49\%$, and $2.21\%$, respectively, in terms of Intersection over Union of SFD. Full article

Deep learning algorithms offer an effective solution to the inefficiencies and poor results of traditional methods for building a footprint extraction from high-resolution remote sensing imagery. However, the heterogeneous shapes and sizes of buildings render local extraction vulnerable to the influence of intricate backgrounds or scenes, culminating in intra-class inconsistency and inaccurate segmentation outcomes. Moreover, the methods for extracting buildings from very high-resolution (VHR) images at present often lose spatial texture information during down-sampling, leading to problems, such as blurry image boundaries or object sticking. To solve these problems, we propose the multi-scale boundary-refined HRNet (MBR-HRNet) model, which preserves detailed boundary features for accurate building segmentation. The boundary refinement module (BRM) enhances the accuracy of small buildings and boundary extraction in the building segmentation network by integrating edge information learning into a separate branch. Additionally, the multi-scale context fusion module integrates feature information of different scales, enhancing the accuracy of the final predicted image. Experiments on WHU and Massachusetts building datasets have shown that MBR-HRNet outperforms other advanced semantic segmentation models, achieving the highest intersection over union results of 91.31% and 70.97%, respectively. Full article

Accurate cropland information is crucial for the assessment of food security and the formulation of effective agricultural policies. Extracting cropland from remote sensing imagery is challenging due to spectral diversity and mixed pixels. Recent advances in remote sensing technology have facilitated the availability of very high-resolution (VHR) remote sensing images that provide detailed ground information. However, VHR cropland extraction in southern China is difficult because of the high heterogeneity and fragmentation of cropland and the insufficient observations of VHR sensors. To address these challenges, we proposed a deep learning-based method for automated high-resolution cropland extraction. The method used an improved HRRS-U-Net model to accurately identify the extent of cropland and explicitly locate field boundaries. The HRRS-U-Net maintained high-resolution details throughout the network to generate precise cropland boundaries. Additionally, the residual learning (RL) and the channel attention mechanism (CAM) were introduced to extract deeper discriminative representations. The proposed method was evaluated over four city-wide study areas (Qingyuan, Yangjiang, Guangzhou, and Shantou) with a diverse range of agricultural systems, using GaoFen-2 (GF-2) images. The cropland extraction results for the study areas had an overall accuracy (OA) ranging from 97.00% to 98.33%, with F1 scores (F1) of 0.830–0.940 and Kappa coefficients (Kappa) of 0.814–0.929. The OA was 97.85%, F1 was 0.915, and Kappa was 0.901 over all study areas. Moreover, our proposed method demonstrated advantages compared to machine learning methods (e.g., RF) and previous semantic segmentation models, such as U-Net, U-Net++, U-Net3+, and MPSPNet. The results demonstrated the generalization ability and reliability of the proposed method for cropland extraction in southern China using VHR remote images. Full article

Deep learning-based algorithms have seen a massive popularity in different areas of remote sensing image analysis over the past decade. Recently, transformer-based architectures, originally introduced in natural language processing, have pervaded computer vision field where the self-attention mechanism has been utilized as a replacement to the popular convolution operator for capturing long-range dependencies. Inspired by recent advances in computer vision, the remote sensing community has also witnessed an increased exploration of vision transformers for a diverse set of tasks. Although a number of surveys have focused on transformers in computer vision in general, to the best of our knowledge we are the first to present a systematic review of recent advances based on transformers in remote sensing. Our survey covers more than 60 recent transformer-based methods for different remote sensing problems in sub-areas of remote sensing: very high-resolution (VHR), hyperspectral (HSI) and synthetic aperture radar (SAR) imagery. We conclude the survey by discussing different challenges and open issues of transformers in remote sensing. Full article

As one of the most important agricultural production types in the world, orchards have high economic, ecological, and cultural value, so the accurate and timely mapping of orchards is highly demanded for many applications. Selecting a remote-sensing (RS) data source is a critical step in efficient orchard mapping, and it is hard to have a RS image with both rich temporal and spatial information. A trade-off between spatial and temporal resolution must be made. Taking grape-growing regions as an example, we tested imagery at different spatial and temporal resolutions as classification inputs (including from Worldview-2, Landsat-8, and Sentinel-2) and compared and assessed their orchard-mapping performance using the same classifier of random forest. Our results showed that the overall accuracies improved from 0.6 to 0.8 as the spatial resolution of the input images increased from 58.86 m to 0.46 m (simulated from Worldview-2 imagery). The overall accuracy improved from 0.7 to 0.86 when the number of images used for classification was increased from 2 to 20 (Landsat-8) or approximately 60 (Sentinel-2) in one year. The marginal benefit of increasing the level of details (LoD) of temporal features on accuracy is higher than that of spatial features, indicating that the classification ability of temporal information is higher than that of spatial information. The highest accuracy of using a very high-resolution (VHR) image can be exceeded only by using four to five medium-resolution multi-temporal images, or even two to three growing season images with the same classifier. Combining the spatial and temporal features from multi-source data can improve the overall accuracies by 5% to 7% compared to using only temporal features. It can also compensate for the accuracy loss caused by missing data or low-quality images in single-source input. Although selecting multi-source data can obtain the best accuracy, selecting single-source data can improve computational efficiency and at the same time obtain an acceptable accuracy. This study provides practical guidance on selecting data at various spatial and temporal resolutions for the efficient mapping of other types of annual crops or orchards. Full article