Catfish density estimation by aerial images analysis and deep learning

This study aimed to propose an approach for orchard trees segmentation using aerial images based on a deep learning convolutional neural network variant, namely the U-net network. The purpose was the automated detection and localization of the canopy of orchard trees under various conditions (i.e., different seasons, different tree ages, different levels of weed coverage). The implemented dataset was composed of images from three different walnut orchards. The achieved variability of the dataset resulted in obtaining images that fell under seven different use cases. The best-trained model achieved 91%, 90%, and 87% accuracy for training, validation, and testing, respectively. The trained model was also tested on never-before-seen orthomosaic images or orchards based on two methods (oversampling and undersampling) in order to tackle issues with out-of-the-field boundary transparent pixels from the image. Even though the training dataset did not contain orthomosaic images, it achieved performance levels that reached up to 99%, demonstrating the robustness of the proposed approach.

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Automatic detection and quantification of floating marine macro-litter in aerial images: Introducing a novel deep learning approach connected to a web application in R

Environmental Pollution ◽

10.1016/j.envpol.2021.116490 ◽

2021 ◽

Vol 273 ◽

pp. 116490

Author(s):

Odei Garcia-Garin ◽

Toni Monleón-Getino ◽

Pere López-Brosa ◽

Asunción Borrell ◽

Alex Aguilar ◽

...

Keyword(s):

Deep Learning ◽

Web Application ◽

Automatic Detection ◽

Aerial Images ◽

Learning Approach ◽

Detection And Quantification

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IMG2nDSM: Height Estimation from Single Airborne RGB Images with Deep Learning

Remote Sensing ◽

10.3390/rs13122417 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2417

Author(s):

Savvas Karatsiolis ◽

Andreas Kamilaris ◽

Ian Cole

Keyword(s):

Deep Learning ◽

State Of The Art ◽

Aerial Imagery ◽

Aerial Images ◽

Surface Model ◽

Large Area ◽

Digital Terrain ◽

Terrain Models ◽

Architectural Features ◽

Rgb Images

Estimating the height of buildings and vegetation in single aerial images is a challenging problem. A task-focused Deep Learning (DL) model that combines architectural features from successful DL models (U-NET and Residual Networks) and learns the mapping from a single aerial imagery to a normalized Digital Surface Model (nDSM) was proposed. The model was trained on aerial images whose corresponding DSM and Digital Terrain Models (DTM) were available and was then used to infer the nDSM of images with no elevation information. The model was evaluated with a dataset covering a large area of Manchester, UK, as well as the 2018 IEEE GRSS Data Fusion Contest LiDAR dataset. The results suggest that the proposed DL architecture is suitable for the task and surpasses other state-of-the-art DL approaches by a large margin.

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Towards Scalable Economic Photovoltaic Potential Analysis Using Aerial Images and Deep Learning

Energies ◽

10.3390/en14133800 ◽

2021 ◽

Vol 14 (13) ◽

pp. 3800

Author(s):

Sebastian Krapf ◽

Nils Kemmerzell ◽

Syed Khawaja Haseeb Khawaja Haseeb Uddin ◽

Manuel Hack Hack Vázquez ◽

Fabian Netzler ◽

...

Keyword(s):

Deep Learning ◽

System Analysis ◽

State Of The Art ◽

Critical Role ◽

Semantic Segmentation ◽

Energy System ◽

Aerial Images ◽

Potential Analysis ◽

3D Data ◽

Challenges And Opportunities

Roof-mounted photovoltaic systems play a critical role in the global transition to renewable energy generation. An analysis of roof photovoltaic potential is an important tool for supporting decision-making and for accelerating new installations. State of the art uses 3D data to conduct potential analyses with high spatial resolution, limiting the study area to places with available 3D data. Recent advances in deep learning allow the required roof information from aerial images to be extracted. Furthermore, most publications consider the technical photovoltaic potential, and only a few publications determine the photovoltaic economic potential. Therefore, this paper extends state of the art by proposing and applying a methodology for scalable economic photovoltaic potential analysis using aerial images and deep learning. Two convolutional neural networks are trained for semantic segmentation of roof segments and superstructures and achieve an Intersection over Union values of 0.84 and 0.64, respectively. We calculated the internal rate of return of each roof segment for 71 buildings in a small study area. A comparison of this paper’s methodology with a 3D-based analysis discusses its benefits and disadvantages. The proposed methodology uses only publicly available data and is potentially scalable to the global level. However, this poses a variety of research challenges and opportunities, which are summarized with a focus on the application of deep learning, economic photovoltaic potential analysis, and energy system analysis.

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Classification of Very-High-Spatial-Resolution Aerial Images Based on Multiscale Features with Limited Semantic Information

Remote Sensing ◽

10.3390/rs13030364 ◽

2021 ◽

Vol 13 (3) ◽

pp. 364

Author(s):

Han Gao ◽

Jinhui Guo ◽

Peng Guo ◽

Xiuwan Chen

Keyword(s):

Deep Learning ◽

Land Cover ◽

Spatial Resolution ◽

Large Scale ◽

High Spatial Resolution ◽

Training Data ◽

Aerial Images ◽

Rural Landscapes ◽

Feature Representations ◽

Object Based

Recently, deep learning has become the most innovative trend for a variety of high-spatial-resolution remote sensing imaging applications. However, large-scale land cover classification via traditional convolutional neural networks (CNNs) with sliding windows is computationally expensive and produces coarse results. Additionally, although such supervised learning approaches have performed well, collecting and annotating datasets for every task are extremely laborious, especially for those fully supervised cases where the pixel-level ground-truth labels are dense. In this work, we propose a new object-oriented deep learning framework that leverages residual networks with different depths to learn adjacent feature representations by embedding a multibranch architecture in the deep learning pipeline. The idea is to exploit limited training data at different neighboring scales to make a tradeoff between weak semantics and strong feature representations for operational land cover mapping tasks. We draw from established geographic object-based image analysis (GEOBIA) as an auxiliary module to reduce the computational burden of spatial reasoning and optimize the classification boundaries. We evaluated the proposed approach on two subdecimeter-resolution datasets involving both urban and rural landscapes. It presented better classification accuracy (88.9%) compared to traditional object-based deep learning methods and achieves an excellent inference time (11.3 s/ha).

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Edge and region segmentation in high-resolution aerial images using improved kernel density estimation: A hybrid approach

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-191547 ◽

2020 ◽

Vol 39 (1) ◽

pp. 543-560

Author(s):

Muthukumaran Malarvel ◽

Soumya Ranjan Nayak

Keyword(s):

High Resolution ◽

Density Estimation ◽

Kernel Density Estimation ◽

Hybrid Approach ◽

Kernel Density ◽

Aerial Images ◽

Region Segmentation

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Deep Learning-Based Object Detection for Unmanned Aerial Systems (UASs)-Based Inspections of Construction Stormwater Practices

Sensors ◽

10.3390/s21082834 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2834

Author(s):

Billur Kazaz ◽

Subhadipto Poddar ◽

Saeed Arabi ◽

Michael A. Perez ◽

Anuj Sharma ◽

...

Keyword(s):

Deep Learning ◽

Object Detection ◽

Pollution Prevention ◽

Aerial Images ◽

Unmanned Aerial Systems ◽

Construction Sites ◽

State Regulations ◽

Novel Approach ◽

Model Training ◽

Aerial Systems

Construction activities typically create large amounts of ground disturbance, which can lead to increased rates of soil erosion. Construction stormwater practices are used on active jobsites to protect downstream waterbodies from offsite sediment transport. Federal and state regulations require routine pollution prevention inspections to ensure that temporary stormwater practices are in place and performing as intended. This study addresses the existing challenges and limitations in the construction stormwater inspections and presents a unique approach for performing unmanned aerial system (UAS)-based inspections. Deep learning-based object detection principles were applied to identify and locate practices installed on active construction sites. The system integrates a post-processing stage by clustering results. The developed framework consists of data preparation with aerial inspections, model training, validation of the model, and testing for accuracy. The developed model was created from 800 aerial images and was used to detect four different types of construction stormwater practices at 100% accuracy on the Mean Average Precision (MAP) with minimal false positive detections. Results indicate that object detection could be implemented on UAS-acquired imagery as a novel approach to construction stormwater inspections and provide accurate results for site plan comparisons by rapidly detecting the quantity and location of field-installed stormwater practices.

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