Towards Amazon Forest Restoration: Automatic Detection of Species from UAV Imagery

Precise assessments of forest species’ composition help analyze biodiversity patterns, estimate wood stocks, and improve carbon stock estimates. Therefore, the objective of this work was to evaluate the use of high-resolution images obtained from Unmanned Aerial Vehicle (UAV) for the identification of forest species in areas of forest regeneration in the Amazon. For this purpose, convolutional neural networks (CNN) were trained using the Keras–Tensorflow package with the faster_rcnn_inception_v2_pets model. Samples of six forest species were used to train CNN. From these, attempts were made with the number of thresholds, which is the cutoff value of the function; any value below this output is considered 0, and values above are treated as an output 1; that is, values above the value stipulated in the Threshold are considered as identified species. The results showed that the reduction in the threshold decreases the accuracy of identification, as well as the overlap of the polygons of species identification. However, in comparison with the data collected in the field, it was observed that there exists a high correlation between the trees identified by the CNN and those observed in the plots. The statistical metrics used to validate the classification results showed that CNN are able to identify species with accuracy above 90%. Based on our results, which demonstrate good accuracy and precision in the identification of species, we conclude that convolutional neural networks are an effective tool in classifying objects from UAV images.

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UAV-BASED 3D URBAN ENVIRONMENT MONITORING

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-3-37-2018 ◽

2018 ◽

Vol IV-3 ◽

pp. 37-43

Author(s):

Wuttichai Boonpook ◽

Yumin Tan ◽

Huaqing Liu ◽

Binbin Zhao ◽

Lingfeng He

Keyword(s):

Change Detection ◽

Detection Method ◽

Three Dimensions ◽

Surface Model ◽

Environment Monitoring ◽

Great Flexibility ◽

Accuracy And Precision ◽

Aerial Vehicle ◽

High Resolution Images ◽

Uav Images

Unmanned Aerial Vehicle (UAV) based remote sensing can be used to make three-dimensions (3D) mapping with great flexibility, besides the ability to provide high resolution images. In this paper we propose a quick-change detection method on UAV images by combining altitude from Digital Surface Model (DSM) and texture analysis from images. Cases of UAV images with and without georeferencing are both considered. Research results show that the accuracy of change detection can be enhanced with georeferencing procedure, and the accuracy and precision of change detection on UAV images which are collected both vertically and obliquely but without georeferencing also have a good performance.

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Fast Automatic Vehicle Detection in UAV Images Using Convolutional Neural Networks

Remote Sensing ◽

10.3390/rs12121994 ◽

2020 ◽

Vol 12 (12) ◽

pp. 1994 ◽

Cited By ~ 1

Author(s):

Xin Luo ◽

Xiaoyue Tian ◽

Huijie Zhang ◽

Weimin Hou ◽

Geng Leng ◽

...

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Vehicle Detection ◽

Neural Computing ◽

Detection Capability ◽

High Processing Speed ◽

Aerial Vehicle ◽

Uav Images ◽

Uav Image ◽

High Processing

Vehicle targets in unmanned aerial vehicle (UAV) images are generally small, so a significant amount of detailed information on targets may be lost after neural computing, which leads to the poor performances of the existing recognition algorithms. Based on convolutional neural networks that utilize the YOLOv3 algorithm, this article focuses on the development of a quick automatic vehicle detection method for UAV images. First, a vehicle dataset for target recognition is constructed. Then, a novel YOLOv3 vehicle detection framework is proposed according to the following characteristics: The vehicle targets in the UAV image are relatively small and dense. The average precision (AP) increased by 5.48%, from 92.01% to 97.49%, which still remains the rather high processing speed of the YOLO network. Finally, the proposed framework is tested using three datasets: COWC, VEDAI, and CAR. The experimental results demonstrate that our method had a better detection capability.

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Ekstraksi Data Bangunan Dari Data Citra Unmanned Aerial Vehicle Menggunakan Metode Convolutional Neural Networks (CNN) (Studi Kasus: Desa Campurejo, Kabupaten Gresik)

Geoid ◽

10.12962/j24423998.v17i1.10289 ◽

2022 ◽

Vol 17 (1) ◽

pp. 81

Author(s):

Citra Ayu Sekar Kinasih ◽

Husnul Hidayat

Keyword(s):

Neural Networks ◽

Unmanned Aerial Vehicle ◽

Convolutional Neural Networks ◽

Aerial Vehicle

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Advances in Convolution Neural Networks Based Crowd Counting and Density Estimation

Big Data and Cognitive Computing ◽

10.3390/bdcc5040050 ◽

2021 ◽

Vol 5 (4) ◽

pp. 50

Author(s):

Rafik Gouiaa ◽

Moulay A. Akhloufi ◽

Mozhdeh Shahbazi

Keyword(s):

Neural Networks ◽

Density Estimation ◽

Traffic Monitoring ◽

Crowd Counting ◽

Convolution Neural Networks ◽

Potential Applications ◽

Comprehensive Survey ◽

Camera Perspective ◽

Aerial Vehicle ◽

Uav Images

Automatically estimating the number of people in unconstrained scenes is a crucial yet challenging task in different real-world applications, including video surveillance, public safety, urban planning, and traffic monitoring. In addition, methods developed to estimate the number of people can be adapted and applied to related tasks in various fields, such as plant counting, vehicle counting, and cell microscopy. Many challenges and problems face crowd counting, including cluttered scenes, extreme occlusions, scale variation, and changes in camera perspective. Therefore, in the past few years, tremendous research efforts have been devoted to crowd counting, and numerous excellent techniques have been proposed. The significant progress in crowd counting methods in recent years is mostly attributed to advances in deep convolution neural networks (CNNs) as well as to public crowd counting datasets. In this work, we review the papers that have been published in the last decade and provide a comprehensive survey of the recent CNNs based crowd counting techniques. We briefly review detection-based, regression-based, and traditional density estimation based approaches. Then, we delve into detail regarding the deep learning based density estimation approaches and recently published datasets. In addition, we discuss the potential applications of crowd counting and in particular its applications using unmanned aerial vehicle (UAV) images.

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UNMANNED AERIAL VEHICLE APPLICATIONS OF 3D MODELLING, VISUALIZATION AND PARAMETER CALCULATIONS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-5-649-2018 ◽

2018 ◽

Vol XLII-5 ◽

pp. 649-652 ◽

Cited By ~ 1

Author(s):

V. Lambey ◽

A. D. Prasad

Keyword(s):

Point Cloud ◽

Low Cost ◽

3D Modelling ◽

Aerial Photogrammetry ◽

Average Accuracy ◽

Aerial Vehicle ◽

High Resolution Images ◽

Institute Of Technology ◽

Uav Images ◽

Economical Alternative

<p><strong>Abstract.</strong> Photogrammetric surveying with the use of Unmanned Aerial Vehicles (UAV) have gained vast popularity in short span. UAV have the potential to provide imagery at an extraordinary spatial and temporal resolution when coupled with remote sensing. Currently, UAV platforms are fastest and easiest source of data for mapping and 3D modelling. It is to be considered as a low-cost substitute to the traditional airborne photogrammetry. In the present study, UAV applications are explored in terms of 3D modelling, visualization and parameter calculations. National Institute of Technology Raipur, Raipur is chosen as study area and high resolution images are acquired from the UAV with 85% overlap. 3D model is processed through the point cloud generated for the UAV images. The results are compared with traditional methods for validation. The average accuracy obtained for elevation points and area is 97.99% and 97.75%. The study proves that UAV based surveying is an economical alternative in terms of money, time and resources, when compared to the classical aerial photogrammetry methods.</p>

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Benchmarking Accuracy and Precision of the Convolutional Neural Networks for Face Recognition on Makeup and Occluded Images

Advances in Intelligent Systems and Computing - ITNG 2021 18th International Conference on Information Technology-New Generations ◽

10.1007/978-3-030-70416-2_58 ◽

2021 ◽

pp. 451-456

Author(s):

Stanislav Selitskiy ◽

Nikolaos Christou ◽

Natalya Selitskaya

Keyword(s):

Neural Networks ◽

Face Recognition ◽

Convolutional Neural Networks ◽

Accuracy And Precision

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Land Cover Classification from fused DSM and UAV Images Using Convolutional Neural Networks

Remote Sensing ◽

10.3390/rs11121461 ◽

2019 ◽

Vol 11 (12) ◽

pp. 1461 ◽

Cited By ~ 18

Author(s):

Husam A. H. Al-Najjar ◽

Bahareh Kalantar ◽

Biswajeet Pradhan ◽

Vahideh Saeidi ◽

Alfian Abdul Halin ◽

...

Keyword(s):

Neural Networks ◽

Land Cover ◽

Convolutional Neural Networks ◽

Land Cover Classification ◽

Training Data ◽

Surface Model ◽

Kappa Index ◽

Diverse Range ◽

Average Accuracy ◽

Uav Images

In recent years, remote sensing researchers have investigated the use of different modalities (or combinations of modalities) for classification tasks. Such modalities can be extracted via a diverse range of sensors and images. Currently, there are no (or only a few) studies that have been done to increase the land cover classification accuracy via unmanned aerial vehicle (UAV)–digital surface model (DSM) fused datasets. Therefore, this study looks at improving the accuracy of these datasets by exploiting convolutional neural networks (CNNs). In this work, we focus on the fusion of DSM and UAV images for land use/land cover mapping via classification into seven classes: bare land, buildings, dense vegetation/trees, grassland, paved roads, shadows, and water bodies. Specifically, we investigated the effectiveness of the two datasets with the aim of inspecting whether the fused DSM yields remarkable outcomes for land cover classification. The datasets were: (i) only orthomosaic image data (Red, Green and Blue channel data), and (ii) a fusion of the orthomosaic image and DSM data, where the final classification was performed using a CNN. CNN, as a classification method, is promising due to hierarchical learning structure, regulating and weight sharing with respect to training data, generalization, optimization and parameters reduction, automatic feature extraction and robust discrimination ability with high performance. The experimental results show that a CNN trained on the fused dataset obtains better results with Kappa index of ~0.98, an average accuracy of 0.97 and final overall accuracy of 0.98. Comparing accuracies between the CNN with DSM result and the CNN without DSM result for the overall accuracy, average accuracy and Kappa index revealed an improvement of 1.2%, 1.8% and 1.5%, respectively. Accordingly, adding the heights of features such as buildings and trees improved the differentiation between vegetation specifically where plants were dense.

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Semi-supervised learning with convolutional neural networks for UAV images automatic recognition

Computers and Electronics in Agriculture ◽

10.1016/j.compag.2019.104932 ◽

2019 ◽

Vol 164 ◽

pp. 104932 ◽

Cited By ~ 7

Author(s):

Willian Paraguassu Amorim ◽

Everton Castelão Tetila ◽

Hemerson Pistori ◽

João Paulo Papa

Keyword(s):

Neural Networks ◽

Supervised Learning ◽

Convolutional Neural Networks ◽

Automatic Recognition ◽

Uav Images

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Automated Detection of Conifer Seedlings in Drone Imagery Using Convolutional Neural Networks

Remote Sensing ◽

10.3390/rs11212585 ◽

2019 ◽

Vol 11 (21) ◽

pp. 2585 ◽

Cited By ~ 13

Author(s):

Michael Fromm ◽

Matthias Schubert ◽

Guillermo Castilla ◽

Julia Linke ◽

Greg McDermid

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Forest Restoration ◽

Oil And Gas ◽

Significant Loss ◽

Tree Regeneration ◽

Training Dataset ◽

Conifer Seedlings ◽

Oil And Gas Exploration ◽

Combining Data

Monitoring tree regeneration in forest areas disturbed by resource extraction is a requirement for sustainably managing the boreal forest of Alberta, Canada. Small remotely piloted aircraft systems (sRPAS, a.k.a. drones) have the potential to decrease the cost of field surveys drastically, but produce large quantities of data that will require specialized processing techniques. In this study, we explored the possibility of using convolutional neural networks (CNNs) on this data for automatically detecting conifer seedlings along recovering seismic lines: a common legacy footprint from oil and gas exploration. We assessed three different CNN architectures, of which faster region-CNN (R-CNN) performed best (mean average precision 81%). Furthermore, we evaluated the effects of training-set size, season, seedling size, and spatial resolution on the detection performance. Our results indicate that drone imagery analyzed by artificial intelligence can be used to detect conifer seedling in regenerating sites with high accuracy, which increases with the size in pixels of the seedlings. By using a pre-trained network, the size of the training dataset can be reduced to a couple hundred seedlings without any significant loss of accuracy. Furthermore, we show that combining data from different seasons yields the best results. The proposed method is a first step towards automated monitoring of forest restoration/regeneration.

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