Explore the vast range of titles available.

Coastal areas harbor the most threatened ecosystems on Earth, and cost-effective ways to monitor and protect them are urgently needed, but they represent a challenge for habitat mapping and multi-temporal observations. The availability of open access, remotely sensed data with increasing spatial and spectral resolution is promising in this context. Thus, in a sector of the Mediterranean coast (Lazio region, Italy), we tested the strength of a phenology-based vegetation mapping approach and statistically compared results with previous studies, making use of open source products across all the processing chain. We identified five accurate land cover classes in three hierarchical levels, with good values of agreement with previous studies for the first and the second hierarchical level. The implemented procedure resulted as being effective for mapping a highly fragmented coastal dune system. This is encouraging to take advantage of the earth observation through remote sensing technology in an open source perspective, even at the fine scale of highly fragmented sand dunes landscapes.

Impervious surface is mainly defined as any surface which water cannot infiltrate the soil. Due to the impact of urban impervious surfaces (UIS) on environmental issues, the amount of impervious surfaces has been recognized as the most significant index of environmental quality. Detection and analysis of impervious surfaces within a watershed is one of the developing areas of scientific interest. This study evaluates and compares the accuracy and performance of five classification algorithms—supervised object-based nearest neighbour (NN) classifier, supervised pixel-based maximum likelihood classifier (MLC), supervised pixel-based spectral angle mapper (SAM), band ratioing normalized difference built-up index (NDBI), and normalized difference impervious index (NDII)—in extracting urban impervious surfaces. Our first aim was to identify the most effective method for mapping UIS using Sentinel-2A and Landsat-8 satellite data. The second aim was to compare and reveal the efficiency of the spatial and spectral resolution of Sentinel-2A and Landsat-8 data in extracting UIS. The results revealed that the supervised object-based NN approach using the visible and near-infrared bands of both satellite imagery produced the most homogenous and accurate map among the other methods. The object-based NN algorithm achieved an overall classification accuracy of 90.91% and 88.64%, and Kappa coefficient of 0.82 and 0.77 for Sentinel-2 and Landsat-8 images, respectively. The study also showed that the Sentinel-2 image yielded better results than the Landsat-8 pan-sharpened image in extracting detail and classification accuracy. Comparing these methods in the selected challenging study area can provide insight into the selection of the classification method for rapid and reliable extraction of UIS.

Spatially explicit information on tree species composition is important for both the forest management and conservation sectors. In combination with machine learning algorithms, very high-resolution satellite imagery may provide an effective solution to reduce the need for labor-intensive and time-consuming field-based surveys. In this study, we evaluated the possibility of using multispectral WorldView-3 (WV-3) satellite imagery for the classification of three main tree species (Quercus robur L., Carpinus betulus L., and Alnus glutinosa (L.) Geartn.) in a lowland, mixed deciduous forest in central Croatia. The pixel-based supervised classification was performed using two machine learning algorithms: random forest (RF) and support vector machine (SVM). Additionally, the contribution of gray level cooccurrence matrix (GLCM) texture features from WV-3 imagery in tree species classification was evaluated. Principal component analysis confirmed GLCM variance to be the most significant texture feature. Of the 373 visually interpreted reference polygons, 237 were used as training polygons and 136 were used as validation polygons. The validation results show relatively high overall accuracy (85%) for tree species classification based solely on WV-3 spectral characteristics and the RF classification approach. As expected, an improvement in classification accuracy was achieved by a combination of spectral and textural features. With the additional use of GLCM variance, the overall accuracy improved by 10% and 7% for RF and SVM classification approaches, respectively.

Mapping coastal dune vegetation is critical to understand dune mobility and resilience in the context of climate change, sea level rise, and increased anthropogenic pressure. However, the identification of plant species from remotely sensed data is tedious and limited to broad vegetation communities, while such environments are dominated by fragmented and small-scale landscape patterns. In June 2019, a comprehensive multi-scale survey including unmanned aerial vehicle (UAV), hyperspectral ground, and airborne data was conducted along approximately 20 km of a coastal dune system in southwest France. The objective was to generate an accurate mapping of the main sediment and plant species ground cover types in order to characterize the spatial distribution of coastal dune stability patterns. Field and UAV data were used to assess the quality of airborne data and generate a robust end-member spectral library. Next, a two-step classification approach, based on the normalized difference vegetation index and Random Forest classifier, was developed. Results show high performances with an overall accuracy of 100% and 92.5% for sand and vegetation ground cover types, respectively. Finally, a coastal dune stability index was computed across the entire study site. Different stability patterns were clearly identified along the coast, highlighting for the first time the high potential of this methodology to support coastal dune management.

Progress in urban climate science is severely restricted by the lack of useful information that describes aspects of the form and function of cities at a detailed spatial resolution. To overcome this shortcoming we are initiating an international effort to develop the World Urban Database and Access Portal Tools (WUDAPT) to gather and disseminate this information in a consistent manner for urban areas worldwide. The first step in developing WUDAPT is a description of cities based on the Local Climate Zone (LCZ) scheme, which classifies natural and urban landscapes into categories based on climate-relevant surface properties. This methodology provides a culturally-neutral framework for collecting information about the internal physical structure of cities. Moreover, studies have shown that remote sensing data can be used for supervised LCZ mapping. Mapping of LCZs is complicated because similar LCZs in different regions have dissimilar spectral properties due to differences in vegetation, building materials and other variations in cultural and physical environmental factors. The WUDAPT protocol developed here provides an easy to understand workflow; uses freely available data and software; and can be applied by someone without specialist knowledge in spatial analysis or urban climate science. The paper also provides an example use of the WUDAPT project results.

Remote sensing data has been widely used for various Earth Observation (EO) missions such as land use and cover classification, weather forecasting, agricultural management, and environmental monitoring. Most existing remote-sensing-data-based models are based on supervised learning that requires large and representative human-labeled data for model training, which is costly and time-consuming. The recent introduction of self-supervised learning (SSL) enables models to learn a representation from orders of magnitude more unlabeled data. The success of SSL is heavily dependent on a pre-designed pretext task, which introduces an inductive bias into the model from a large amount of unlabeled data. Since remote sensing imagery has rich spectral information beyond the standard RGB color space, it may not be straightforward to extend to the multi/hyperspectral domain the pretext tasks established in computer vision based on RGB images. To address this challenge, this work proposed a generic self-supervised learning framework based on remote sensing data at both the object and pixel levels. The method contains two novel pretext tasks, one for object-based and one for pixel-based remote sensing data analysis methods. One pretext task is used to reconstruct the spectral profile from the masked data, which can be used to extract a representation of pixel information and improve the performance of downstream tasks associated with pixel-based analysis. The second pretext task is used to identify objects from multiple views of the same object in multispectral data, which can be used to extract a representation and improve the performance of downstream tasks associated with object-based analysis. The results of two typical downstream task evaluation exercises (a multilabel land cover classification task on Sentinel-2 multispectral datasets and a ground soil parameter retrieval task on hyperspectral datasets) demonstrate that the proposed SSL method learns a target representation that covers both spatial and spectral information from massive unlabeled data. A comparison with currently available SSL methods shows that the proposed method, which emphasizes both spectral and spatial features, outperforms existing SSL methods on multi- and hyperspectral remote sensing datasets. We believe that this approach has the potential to be effective in a wider range of remote sensing applications and we will explore its utility in more remote sensing applications in the future.

Malware analytics suffer from scarce, delayed, and privacy-constrained labels, limiting fully supervised detection and hampering responsiveness to zero-day threats. We propose SMAD, a Semi-supervised Android Malicious App Detector that integrates a segmentation-oriented backbone—to extract pixel-level, multi-scale features from APK imagery—with a dual-branch consistency objective that enforces predictive agreement between two parallel branches on the same image. We evaluate SMAD on CICMalDroid2020 under label budgets of 0.5, 0.25, and 0.125 and show that it achieves higher accuracy, macro-precision, macro-recall, and macro-F1 with smoother learning curves than supervised training, a recursive pseudo-labeling baseline, a FixMatch baseline, and a confidence-thresholded consistency ablation. A backbone ablation (replacing the dense encoder with WideResNet) indicates that pixel-level, multi-scale features under agreement contribute substantially to these gains. We observe a coverage–precision trade-off: hard confidence gating filters noise but lowers early-training performance, whereas enforcing consistency on dense, pixel-level representations yields sustained label-efficiency gains for image-based malware detection. Consequently, SMAD offers a practical path to high-utility detection under tight labeling budgets—a setting common in real-world security applications.

Weakly supervised semantic segmentation (WSSS), training segmentation models via image-level labels, has the advantage of low manual annotation cost compared with fully supervised semantic segmentation. However, the masks generated by the currently fashionable methods based on the Class Activation Map (CAM) still have the defects of low target segmentation accuracy, many noise pixels and incorrectly activated target pixels. To handle these shortages, the paper proposes a novel WSSS integration network (denoted as WintN-CSG) by adequately fusing the merits of scalability and versatility of multimodal pre-trained basic models CLIP, SAM as well as Grounding-DINO. The superiority of this network with CLIP as a basic framework also benefits from the creative development of Attention Selection Class-aware Attention-based Affinity (AS-CAA), Box Mask Denoising (BMD), and SAM Mask Selection and Fusion (SMSF) modules. Specifically, the proposed AS-CAA can effectively select the representative attention weight maps in Multi-Head Self-Attention (MHSA) to preliminarily remove noise pixels and modify incorrectly activated pixels. Subsequently, the designed BMD combined with Grounding-DINO can shield all noise pixels outside the bounding box, and accurately refine the isolated pixels inside the bounding box, improving the integrity and accuracy of the mask. Furthermore, the deployed SMSF screens out the most suitable masks among many superior mask candidates generated by SAM and makes up for the missing target pixels with the help of fusion and activation algorithms. Finally, experiments with only image-level labels on the PASCAL VOC 2012, MS COCO 2014 and CitySpace datasets show that our scheme achieves excellent performance in the efficiency of mask generation and segmentation accuracy.

The aim of this study is to conduct accuracy analyses of Land Use Land Cover (LULC) classifications derived from Sentinel-2 and Landsat-8 data, and to reveal which dataset present better accuracy results. Zonguldak city and its near surrounding was selected as study area for this case study. Sentinel-2 Multispectral Instrument (MSI) and Landsat-8 the Operational Land Imager (OLI) data, acquired on 6 April 2016 and 3 April 2016 respectively, were utilized as satellite imagery in the study. The RGB and NIR bands of Sentinel-2 and Landsat-8 were used for classification and comparison. Pan-sharpening process was carried out for Landsat-8 data before classification because the spatial resolution of Landsat-8 (30m) is far from Sentinel-2 RGB and NIR bands (10m). LULC images were generated using pixel-based Maximum Likelihood (MLC) supervised classification method. As a result of the accuracy assessment, kappa statistics for Sentinel-2 and Landsat-8 data were 0.78 and 0.85 respectively. The obtained results showed that Sentinel-2 MSI presents more satisfying LULC images than Landsat-8 OLI data. However, in some areas of Sea class Landsat-8 presented better results than Sentinel-2.

This article describes a comparison of the pixel-based classification methods used to distinguish ice from other land cover types. The article focuses on processing RGB imagery, as these are very easy to obtained. The imagery was taken using UAVs and has a very high spatial resolution. Classical classification methods (ISODATA and Maximum Likelihood) and more modern approaches (support vector machines, random forests, deep learning) have been compared for image data classifications. Input datasets were created from two distinct areas: The Pond Skříň and the Baroch Nature Reserve. The images were classified into two classes: ice and all other land cover types. The accuracy of each classification was verified using a Cohen’s Kappa coefficient, with reference values obtained via manual surface identification. Deep learning and Maximum Likelihood were the best classifiers, with a classification accuracy of over 92% in the first area of interest. On average, the support vector machine was the best classifier for both areas of interest. A comparison of the selected methods, which were applied to highly detailed RGB images obtained with UAVs, demonstrates the potential of their utilization compared to imagery obtained using satellites or aerial technologies for remote sensing.