Empowering Conservation through Deep Convolutional Neural Networks and Unmanned Aerial Systems
Tropical rainforests worldwide are negatively impacted from a variety of human-caused threats. Unfortunately, our ability to study these rainforests is impeded by logistical problems such as their physical inaccessibility, expensive aerial imagery, and/or coarse satellite data. One solution is the u...
Main Author: | |
---|---|
Format: | Doctoral or Postdoctoral Thesis |
Language: | English |
Published: |
eScholarship, University of California
2018
|
Subjects: | |
Online Access: | http://www.escholarship.org/uc/item/5rh083rc |
id |
ftcdlib:qt5rh083rc |
---|---|
record_format |
openpolar |
spelling |
ftcdlib:qt5rh083rc 2023-05-15T15:11:29+02:00 Empowering Conservation through Deep Convolutional Neural Networks and Unmanned Aerial Systems Epperson, Matthew 57 2018-01-01 application/pdf http://www.escholarship.org/uc/item/5rh083rc en eng eScholarship, University of California http://www.escholarship.org/uc/item/5rh083rc qt5rh083rc public Epperson, Matthew. (2018). Empowering Conservation through Deep Convolutional Neural Networks and Unmanned Aerial Systems. UC San Diego: Electrical Engineering (Intelsys, Robotics and Cont). Retrieved from: http://www.escholarship.org/uc/item/5rh083rc Computer science Electrical engineering Wildlife conservation CNN computer vision conservation deep learning ecology machine learning dissertation 2018 ftcdlib 2018-05-18T22:51:55Z Tropical rainforests worldwide are negatively impacted from a variety of human-caused threats. Unfortunately, our ability to study these rainforests is impeded by logistical problems such as their physical inaccessibility, expensive aerial imagery, and/or coarse satellite data. One solution is the use of low-cost, Unmanned Aerial Vehicles (UAV), commonly referred to as drones. Drones are now widely recognized as a tool for ecology, environmental science, and conservation, collecting imagery that is superior to satellite data in resolution. We asked: Can we take advantage of the sub-meter, high-resolution imagery to detect specific tree species or groups, and use these data as indicators of rainforest functional traits and characteristics? We demonstrate a low-cost method for obtaining high-resolution aerial imagery in a rainforest of Belize using a drone over three sites in two rainforest protected areas. We built a workflow that uses Structure from Motion (SfM) on the drone images to create a large orthomosaic and a Deep Convolutional Neural Network (CNN) to classify indicator tree species. We selected: 1) Cohune Palm (Attalea cohune) as they are indicative of past disturbance and current soil condition; and, 2) the dry-season deciduous tree group since deciduousness is an important ecological factor of rainforest structure and function. This framework serves as a guide for tackling difficult ecological challenges and we show two additionally examples of how a similar architecture can help count wildlife populations in the Arctic. Doctoral or Postdoctoral Thesis Arctic University of California: eScholarship Arctic |
institution |
Open Polar |
collection |
University of California: eScholarship |
op_collection_id |
ftcdlib |
language |
English |
topic |
Computer science Electrical engineering Wildlife conservation CNN computer vision conservation deep learning ecology machine learning |
spellingShingle |
Computer science Electrical engineering Wildlife conservation CNN computer vision conservation deep learning ecology machine learning Epperson, Matthew Empowering Conservation through Deep Convolutional Neural Networks and Unmanned Aerial Systems |
topic_facet |
Computer science Electrical engineering Wildlife conservation CNN computer vision conservation deep learning ecology machine learning |
description |
Tropical rainforests worldwide are negatively impacted from a variety of human-caused threats. Unfortunately, our ability to study these rainforests is impeded by logistical problems such as their physical inaccessibility, expensive aerial imagery, and/or coarse satellite data. One solution is the use of low-cost, Unmanned Aerial Vehicles (UAV), commonly referred to as drones. Drones are now widely recognized as a tool for ecology, environmental science, and conservation, collecting imagery that is superior to satellite data in resolution. We asked: Can we take advantage of the sub-meter, high-resolution imagery to detect specific tree species or groups, and use these data as indicators of rainforest functional traits and characteristics? We demonstrate a low-cost method for obtaining high-resolution aerial imagery in a rainforest of Belize using a drone over three sites in two rainforest protected areas. We built a workflow that uses Structure from Motion (SfM) on the drone images to create a large orthomosaic and a Deep Convolutional Neural Network (CNN) to classify indicator tree species. We selected: 1) Cohune Palm (Attalea cohune) as they are indicative of past disturbance and current soil condition; and, 2) the dry-season deciduous tree group since deciduousness is an important ecological factor of rainforest structure and function. This framework serves as a guide for tackling difficult ecological challenges and we show two additionally examples of how a similar architecture can help count wildlife populations in the Arctic. |
format |
Doctoral or Postdoctoral Thesis |
author |
Epperson, Matthew |
author_facet |
Epperson, Matthew |
author_sort |
Epperson, Matthew |
title |
Empowering Conservation through Deep Convolutional Neural Networks and Unmanned Aerial Systems |
title_short |
Empowering Conservation through Deep Convolutional Neural Networks and Unmanned Aerial Systems |
title_full |
Empowering Conservation through Deep Convolutional Neural Networks and Unmanned Aerial Systems |
title_fullStr |
Empowering Conservation through Deep Convolutional Neural Networks and Unmanned Aerial Systems |
title_full_unstemmed |
Empowering Conservation through Deep Convolutional Neural Networks and Unmanned Aerial Systems |
title_sort |
empowering conservation through deep convolutional neural networks and unmanned aerial systems |
publisher |
eScholarship, University of California |
publishDate |
2018 |
url |
http://www.escholarship.org/uc/item/5rh083rc |
op_coverage |
57 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Epperson, Matthew. (2018). Empowering Conservation through Deep Convolutional Neural Networks and Unmanned Aerial Systems. UC San Diego: Electrical Engineering (Intelsys, Robotics and Cont). Retrieved from: http://www.escholarship.org/uc/item/5rh083rc |
op_relation |
http://www.escholarship.org/uc/item/5rh083rc qt5rh083rc |
op_rights |
public |
_version_ |
1766342326857236480 |