High-resolution Aerial Images Research Articles

Deep Learning based Semantic Segmentation for Buildings Detection from Remote Sensing Images

Building extraction from remote sensing images is the process of automatically identifying and extracting the boundaries of buildings from high-resolution aerial or satellite images. The extracted building footprints can be used for a variety of applications, such as urban planning, disaster management, city development, land management, environmental monitoring, and 3D modeling. The results of building extraction from remote sensing images depend on several factors, such as the quality and resolution of the image and the choice of algorithm.The process of building extraction from remote sensing images typically involves a series of steps, including image pre-processing, feature extraction, and classification. Building extraction from remote sensing images can be challenging due to factors such as varying building sizes and shapes, shadows, and occlusions. However, recent advances in deep learning and computer vision techniques have led to significant improvements in the accuracy and efficiency of building extraction methods. This research presents a deep learning semantic segmentation architecture-based model for developing building detection from high resolution remote sensing images. The open-source Massachusetts dataset is used to train the suggested UNet architecture. The model is optimized using the RMSProp algorithm with a learning rate of 0.0001 for 100 epochs. After 1.52 hours of training on Google Colab the model achieved an 83.55% F1 score, which indicates strong precision and recall.

PCINet: a Prototype- and Concept-based Interpretable Network for Mutli-scene Recognition

Abstract. With the development of remote sensing techniques, a large number of high-resolution aerial images is now available and benefit many applications. Multi-scene recognition plays a key role in applying remote sensing images to these applications, which refers to predicting multiple scenes coexisted in an aerial image and has attracted an increasing attention. Recently, most researchers tend to invent deep learning-based recognition models and has gained great achievements. However, few efforts have been deployed to explaining the success of deep neural networks in multi-scene recognition. To address this, we introduce concept bottleneck model (CBM) to interpreting model performance and propose a novel network, namely Prototype- and Concept-based Interpretable Network (PCINet), that projects aerial imagery into a prototype-concept memory bank and encode their correlations for explaining how a network can identify coexisting scenes in an aerial image. Specifically, the proposed network mainly consists of two branches: prototype matching that measures similarity scores between image features and scene prototypes, and concept bottleneck branches that aligned image features to textual embeddings and compute their relations with concept embeddings. Afterwards, Outputs are integrated for inferring scene categories. Experimental results show that the model enhances interpretability, providing valuable insights for urban planning and resource management, thereby bridging the gap between deep learning models and practical applications.

The Importance of Ground Control Points In A Photogrammetric Workflow

Abstract This study investigates the optimal distribution and pattern of ground control points (GCPs) in aerial photogrammetric projects. Aerial triangulation (AT), also known as bundle adjustment, is the fundamental step in refining 3D reconstruction models and camera positions, thereby minimizing reprojection errors. The study utilizes data from a national project in Romania, employing high-resolution aerial images acquisition using photogrammetric sensors. The project has rigorous requirements of ground control points (GCP) placement and field measurements using GNSS and geometric leveling techniques. The study employs various scenarios, manipulating the number and distribution of GCPs, to assess their influence on planimetric and altimetric accuracy. Results indicate that the configuration and number of GCPs significantly affect the accuracy of photogrammetric products, such as dense image point clouds, digital surface models, and orthophotos. Moreover, the study underscores the importance of precise GCP determination methods, especially in regions lacking a precise gravimetric geoid model. In scenarios with inadequate GCP coverage the outcomes have inferior quality, emphasizing the critical role of GCPs in ensuring the quality of photogrammetric products. Overall, the research gives a clear view on the best placement patterns of GCPs and their influence on AT process evaluation performed in check points (CHKs).

Triangular Greenness Index Analysis for Monitoring Fungal Disease in Pine Trees: A UAV-based Approach

The Triangular Greenness Index (TGI) is a vegetation index derived from high-resolution aerial images acquired using unmanned aerial vehicles (UAVs). It serves as a valuable tool for quantifying vegetation health and dynamics in the visible spectrum. The TGI combines key components, including red reflectance and green reflectance, extracted from UAV-based imagery. The red component represents chlorophyll absorption and photosynthetic activity, while the green component reflects vegetation density and canopy structure. By integrating these components, the TGI offers a comprehensive measure of photosynthetically active vegetation, utilizing UAVs as a data collection platform. This study highlight the importance of the TGI derived from UAV-based imagery in monitoring vegetation changes, assessing ecosystem responses, and tracking variations in land cover and biodiversity. Furthermore, the application of TGI analysis using UAV-based aerial imagery shows promise in accurately identifying and monitoring vegetation affected by fungal diseases. This integrated approach enables the detection of diseased trees based on distinct changes in greenness observed in their foliage. Because fungal diseases dry the plant and cause the green areas to disappear. The integration of UAV technology enhances the accuracy and efficiency of TGI calculation, contributing to effective management and conservation strategies in the context of fungal disease detection in vegetation. In this study, TGI was produced using UAV-based orthophoto and healthy and sick trees were determined. According to the accuracy analysis, producer accuracy for detecting green plants was 99.7% and user accuracy was 98.5%. Fungal disease could be detected with 98.5% producer accuracy and 96.5% user accuracy. The overall accuracy of the study was calculated as 98.6%.

Extraction of Water Bodies from High-Resolution Aerial and Satellite Images Using Visual Foundation Models

Water, indispensable for life and central to ecosystems, human activities, and climate dynamics, requires rapid and accurate monitoring. This is vital for sustaining ecosystems, enhancing human welfare, and effectively managing land, water, and biodiversity on both the local and global level. In the rapidly evolving domain of remote sensing and deep learning, this study focuses on water body extraction and classification through the use of recent deep learning models of visual foundation models (VFMs). Specifically, the Segment Anything Model (SAM) and Contrastive Language-Image Pre-training (CLIP) models have shown promise in semantic segmentation, dataset creation, change detection, and instance segmentation tasks. A novel two-step approach involving segmenting images via the Automatic Mask Generator method of the SAM and the zero-shot classification of segments using CLIP is proposed, and its effectiveness is tested on water body extraction problems. The proposed methodology was applied to both remote sensing imagery acquired from LANDSAT 8 OLI and very high-resolution aerial imagery. Results revealed that the proposed methodology accurately delineated water bodies across complex environmental conditions, achieving a mean intersection over union (IoU) of 94.41% and an F1 score of 96.97% for satellite imagery. Similarly, for the aerial imagery dataset, the proposed methodology achieved a mean IoU of 90.83% and an F1 score exceeding 94.56%. The high accuracy achieved in selecting segments predominantly classified as water highlights the effectiveness of the proposed model in intricate environmental image analysis.

Tree Crown Detection using UAV-captured High-Resolution Aerial Images for Baghdad University Campus

The research paper's objective is to develop an automated system that uses high-resolution aerial images captured by Unmanned Aerial Vehicles (UAVs) to accurately detect and analyze tree crowns on the Baghdad University campus. This analysis of tree crowns is crucial for assessing vegetation health and distribution in urban areas, with applications in urban planning, ecology, and environmental studies. The proposed system utilizes computer vision and machine learning techniques to detect and delineate tree crowns, facilitating objective analysis of tree canopy coverage. The study employed a Phantom Four drone flying at a height of 55 meters with an 80% overlap in image coverage. Ground control points were established using the Topcon_HIPER2 device and Static GPS Surveying Techniques. The flight plan was designed with PIX4Dcapture software, and the resulting ortho-map had a 2 cm resolution. The research used the YOLOv3 artificial intelligence technique for tree detection after training a customized dataset. The model achieved impressive performance metrics, including a low loss function value of 0.023, a high SDC value of 0.984, and a robust Jaccard Index value of 0.943.

Generating Pixel Enhancement for Road Extraction in High-Resolution Aerial Images

Generating Pixel Enhancement for Road Extraction in High-Resolution Aerial Images

A High Spatial Resolution Aerial Image Dataset and an Efficient Scene Classification Model

A High Spatial Resolution Aerial Image Dataset and an Efficient Scene Classification Model

Paper] Deep Learning based Hierarchical Object Detection System Adopting a Depth Correction Scheme for High-Resolution Aerial Images Towards Realization of Autonomous UAV Flight

Paper] Deep Learning based Hierarchical Object Detection System Adopting a Depth Correction Scheme for High-Resolution Aerial Images Towards Realization of Autonomous UAV Flight

Accurate Detection and Tracking of Small-Scale Vehicles in High-Altitude Unmanned Aerial Vehicle Bird-View Imagery

Vehicle detection and tracking from unmanned aerial vehicles (UAVs) aerial images are among the main tasks of intelligent traffic systems. Especially in tasks with long distances, extensive backgrounds, and small objects, it increases the difficulty of localization and regression, which can easily lead to missed detections and false positives. This paper proposes a detection-based small-scale vehicle tracking framework that integrates an improved YOLOX network and the DeepSORT algorithm to address these issues. Based on the original YOLOX network, a shallow feature extraction network, 160 × 160 pixels, is added to enhance the ability to extract small-scale object features. A convolutional block attention module (CBAM) is inserted in front of the neck network to select crucial information for vehicle detection tasks while suppressing noncritical ones. EIoU_Loss is introduced as the bounding box regression loss function in training to speed up their convergence and improve the localization accuracy of the small objects. Furthermore, an image segmentation method is proposed to effectively reduce missed and false detection events. It divides the original high-definition image into multiple subimages, first detected and then reassembled. Finally, the improved YOLOX network is used as the detector of the DeepSORT to perform small-scale vehicle detection and tracking tasks in various traffic scenarios. Experiments show that the proposed method can significantly improve the detection accuracy of the network and effectively solve the problems of missed detection and false positives in small-scale vehicle tracking tasks in high-resolution aerial images captured by high-altitude UAVs. Significantly, the algorithm proposed in this paper has sufficient robustness for small-scale tracking tasks of aerial videos captured at different altitudes.

Self-Adaptive-Filling Deep Convolutional Neural Network Classification Method for Mountain Vegetation Type Based on High Spatial Resolution Aerial Images

The composition and structure of mountain vegetation are complex and changeable, and thus urgently require the integration of Object-Based Image Analysis (OBIA) and Deep Convolutional Neural Networks (DCNNs). However, while integration technology studies are continuing to increase, there have been few studies that have carried out the classification of mountain vegetation by combining OBIA and DCNNs, for it is difficult to obtain enough samples to trigger the potential of DCNNs for mountain vegetation type classification, especially using high-spatial-resolution remote sensing images. To address this issue, we propose a self-adaptive-filling method (SAF) to incorporate the OBIA method to improve the performance of DCNNs in mountain vegetation type classification using high-spatial-resolution aerial images. Using this method, SAF technology was employed to produce enough regular sample data for DCNNs by filling the irregular objects created by image segmenting using interior adaptive pixel blocks. Meanwhile, non-sample segmented image objects were shaped into different regular rectangular blocks via SAF. Then, the classification result was defined by voting combining the DCNN performance. Compared to traditional OBIA methods, SAF generates more samples for the DCNN and fully utilizes every single pixel of the DCNN input. We design experiments to compare them with traditional OBIA and semantic segmentation methods, such as U-net, MACU-net, and SegNeXt. The results show that our SAF-DCNN outperforms traditional OBIA in terms of accuracy and it is similar to the accuracy of the best performing method in semantic segmentation. However, it reduces the common pretzel phenomenon of semantic segmentation (black and white noise generated in classification). Overall, the SAF-based OBIA using DCNNs, which is proposed in this paper, is superior to other commonly used methods for vegetation classification in mountainous areas.

High-Resolution Aerial Image by Placing a Ball Lens at a Virtually Conjugate Position in AIRR Optical System

High-Resolution Aerial Image by Placing a Ball Lens at a Virtually Conjugate Position in AIRR Optical System

Evaluating the effects of texture features on Pinus sylvestris classification using high-resolution aerial imagery

Pinus sylvestris, also known as Scots pine, is the most widespread type of pine tree found around the world. Texture features can recognize patterns in an image's texture and so distinguish different species with similar spectral properties. They also record information about the spatial layout of pixels inside an image. Nonetheless, previous research has not addressed the effects of textural variables on Scots pine classification using high-resolution aerial images. In the context of this investigation, we used TreeSatAI, a benchmark dataset with categorized high-resolution aerial imagery, to conduct a comparative analysis, and we examined a comprehensive set of 93 texture features (radiomics) by generating several parameters such as First Order, Gray Level Co-occurrence Matrix (GLCM), Gray Level Dependence Matrix (GLDM), Gray Level Size Zone Matrix (GLSZM), and Neighboring Gray Tone Difference Matrix (NGTDM). We proceeded to train machine learning models utilizing the state-of-the-art algorithms such as XGBoost, LightGBM, and Gradient Boosting Machines (GBM). Upon conducting a more comprehensive analysis, it has come to light that the GLDM and GLRLM features have exhibited greater significance in the realm of satellite image processing research, specifically pertaining to the precise classification of Scots pine. This finding challenges the conventional reliance on GLCM features, which have traditionally been favored in similar studies. This study is remarkable due to its emphasis on the efficacy of employing high-resolution aerial imagery and texture features within the realm of satellite image processing applications.

Improving the performance of learned descriptors in the matching of high spatial resolution aerial images by proposing a large-scale dataset of vertical images

Improving the performance of learned descriptors in the matching of high spatial resolution aerial images by proposing a large-scale dataset of vertical images

Feasibility of Detecting Sweet Potato (Ipomoea batatas) Virus Disease from High-Resolution Imagery in the Field Using a Deep Learning Framework

The sweet potato is an essential food and economic crop that is often threatened by the devastating sweet potato virus disease (SPVD), especially in developing countries. Traditional laboratory-based direct detection methods and field scouting are commonly used to rapidly detect SPVD. However, these molecular-based methods are costly and disruptive, while field scouting is subjective, labor-intensive, and time-consuming. In this study, we propose a deep learning-based object detection framework to assess the feasibility of detecting SPVD from ground and aerial high-resolution images. We proposed a novel object detector called SPVDet, as well as a lightweight version called SPVDet-Nano, using a single-level feature. These detectors were prototyped based on a small-scale publicly available benchmark dataset (PASCAL VOC 2012) and compared to mainstream feature pyramid object detectors using a leading large-scale publicly available benchmark dataset (MS COCO 2017). The learned model weights from this dataset were then transferred to fine-tune the detectors and directly analyze our self-made SPVD dataset encompassing one category and 1074 objects, incorporating the slicing aided hyper inference (SAHI) technology. The results showed that SPVDet outperformed both its single-level counterparts and several mainstream feature pyramid detectors. Furthermore, the introduction of SAHI techniques significantly improved the detection accuracy of SPVDet by 14% in terms of mean average precision (mAP) in both ground and aerial images, and yielded the best detection accuracy of 78.1% from close-up perspectives. These findings demonstrate the feasibility of detecting SPVD from ground and unmanned aerial vehicle (UAV) high-resolution images using the deep learning-based SPVDet object detector proposed here. They also have great implications for broader applications in high-throughput phenotyping of sweet potatoes under biotic stresses, which could accelerate the screening process for genetic resistance against SPVD in plant breeding and provide timely decision support for production management.

Deep Learning Approach for Multi-class Semantic Segmentation of UAV Images

Image understanding plays a very crucial role in remote sensing applications. For this, image semantic segmentation is one of the approaches where each pixel of an image is assigned to particular classes based on various features. Aerial semantic segmentation suffers from the class imbalance problem. Proper differentiation of least represented categories is challenging and a goal for the state-of-art approach. In this work, we present a novel deep learning method to perform this task. We proposed a lightweight encoder-decoder network residual depth separable UNet (RDS-UNet) and conditional random field for effective segmentation on very high-resolution aerial images. We proposed patch-with-multi-class sampling to handle the class imbalance problem without increasing the computational overhead during the training process. We created a semi-precise annotated UAV dataset named NESAC UAV Seg for the aerial semantic segmentation task. We demonstrated the efficacy of our model using the publicly available benchmark Drone Deploy dataset and our NESAC UAV Seg dataset. Our model required approximately half the number of trainable parameters and floating point operations compared to other methods. A detailed ablation study is presented to showcase the effectiveness of various modules utilized in our network.

Forest canopy mortality during the 2018-2020 summer drought years in Central Europe: The application of a deep learning approach on aerial images across Luxembourg

Abstract Efficient monitoring of tree canopy mortality requires data that cover large areas and capture changes over time while being precise enough to detect changes at the canopy level. In the development of automated approaches, aerial images represent an under-exploited scale between high-resolution drone images and satellite data. Our aim herein was to use a deep learning model to automatically detect canopy mortality from high-resolution aerial images after severe drought events in the summers 2018–2020 in Luxembourg. We analysed canopy mortality for the years 2017–2020 using the EfficientUNet++, a state-of-the-art convolutional neural network. Training data were acquired for the years 2017 and 2019 only, in order to test the robustness of the model for years with no reference data. We found a severe increase in canopy mortality from 0.64 km2 in 2017 to 7.49 km2 in 2020, with conifers being affected at a much higher rate than broadleaf trees. The model was able to classify canopy mortality with an F1-score of 66%–71% and we found that for years without training data, we were able to transfer the model trained on other years to predict canopy mortality, if illumination conditions did not deviate severely. We conclude that aerial images hold much potential for automated regular monitoring of canopy mortality over large areas at canopy level when analysed with deep learning approaches. We consider the suggested approach a cost-efficient and -effective alternative to drone and field-based sampling.

Weed mapping in multispectral drone imagery using lightweight vision transformers

In precision agriculture, non-invasive remote sensing can be used to observe crops and weeds in visible and non-visible spectra. This paper proposes a novel approach for weed mapping using lightweight Vision Transformers. The method uses a lightweight Transformer architecture to process high-resolution aerial images obtained from drones and performs semantic segmentation to distinguish between crops and weeds. The method also employs specific architectural designs to enable transfer learning from RGB weights in a multispectral setting. For this purpose, the WeedMap dataset, acquired by drones equipped with multispectral cameras, was used. The experimental results demonstrate the effectiveness of the proposed method, exceeding the state-of-the-art. Our approach also enables more efficient mapping, allowing farmers to quickly and easily identify infested areas and prioritize their control efforts. These results encourage using drones as versatile computer vision flying devices for herbicide management, thereby improving crop yields. The code is available at https://github.com/pasqualedem/LWViTs-for-weedmapping.

Aedes aegypti abundance in urban neighborhoods of Maricopa County, Arizona, is linked to increasing socioeconomic status and tree cover

BackgroundUnderstanding coupled human-environment factors which promote Aedes aegypti abundance is critical to preventing the spread of Zika, chikungunya, yellow fever and dengue viruses. High temperatures and aridity theoretically make arid lands inhospitable for Ae. aegypti mosquitoes, yet their populations are well established in many desert cities.MethodsWe investigated associations between socioeconomic and built environment factors and Ae. aegypti abundance in Maricopa County, Arizona, home to Phoenix metropolitan area. Maricopa County Environmental Services conducts weekly mosquito surveillance with CO2-baited Encephalitis Vector Survey or BG-Sentinel traps at > 850 locations throughout the county. Counts of adult female Ae. aegypti from 2014 to 2017 were joined with US Census data, precipitation and temperature data, and 2015 land cover from high-resolution (1 m) aerial images from the National Agricultural Imagery Program.ResultsFrom 139,729 trap-nights, 107,116 Ae. aegypti females were captured. Counts were significantly positively associated with higher socioeconomic status. This association was partially explained by higher densities of non-native landscaping in wealthier neighborhoods; a 1% increase in the density of tree cover around the trap was associated with a ~ 7% higher count of Ae. aegypti (95% CI: 6–9%).ConclusionsMany models predict that climate change will drive aridification in some heavily populated regions, including those where Ae. aegypti are widespread. City climate change adaptation plans often include green spaces and vegetation cover to increase resilience to extreme heat, but these may unintentionally create hospitable microclimates for Ae. aegypti. This possible outcome should be addressed to reduce the potential for outbreaks of Aedes-borne diseases in desert cities.Graphical

Rethinking urban riparian ecosystems as a frontline strategy to counter climate change: mapping 60 years of carbon sequestration evolution in Padua, Italy

Nature-based solutions (NBSs) are considered essential measures to face climate change in cities. Riparian ecosystems are often undervalued as NBSs even if they represent an “already plugged-in” ecosystem service provider. However, applied research on urban riparian ecosystems is scarce. To fill the gap, this study frames the role of these ecosystems as a frontline mitigation and adaptation strategy by (i) investigating how urban riparian ecosystems experienced a process of urbanization from World War II until today in Padua (Italy), (ii) estimating how effective was carbon sequestration, and (iii) assessing how an afforestation process might contribute to mitigation scenarios. Land use/land cover changes are first mapped with high-resolution aerial images and, by using InVEST models, carbon sequestration is estimated. Results highlight that impervious surfaces notably increased along these ecosystems; hence, carbon sequestration was negative (−928 Mg/C). The future scenario (2022–2050) indicates an increase of carbon stocks (+4,329.95 Mg/C), giving a contribution toward city carbon neutrality. Results suggest that, in the recent past, the urban management and policies did not provide clear and well-defined planning of these areas. On the contrary, at present, urban riparian ecosystems might represent already existing multi-functional NBSs to be restored and enhanced within cities for climate-resilient planning and for increasing the life quality of city dwellers. Finally, our methodology provides a replicable spatial database for supporting urban planning policies.