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Kyle Landolt

Kyle is a geographer in the Geospatial Sciences and Technologies branch at the Upper Midwest Environmental Sciences Center. His current work uses geospatial statistics and machine learning techniques to process aerial imagery for natural resource management.

Research Interests:

  • Data Management
  • Open Source Software
  • Geographic Information Systems
  • Machine Learning
  • Cartography

Education and Certifications

  • M.S., Geography, The University of Tennessee, Knoxville, 2016

  • B.S., Ecological Restoration, Texas A&M University, 2014

Science and Products

link
An aerial image of a flock of waterfowl floating on the ocean.

Deep Learning for Automated Detection and Classification of Waterfowl, Seabirds, and other Wildlife from Digital Aerial Imagery

The U.S. Geological Survey Upper Midwest Environmental Sciences Center is developing deep learning algorithms and tools for the automatic detection, enumeration, classification, and annotation of seabirds and other marine wildlife from digital aerial imagery — advancing cutting-edge research in collaboration with the Bureau of Ocean Energy Management (BOEM), the U.S. Fish and Wildlife Service (FWS...
By
Ecosystems Mission Area, Species Management Research Program, Upper Midwest Environmental Sciences Center
link

Deep Learning for Automated Detection and Classification of Waterfowl, Seabirds, and other Wildlife from Digital Aerial Imagery

The U.S. Geological Survey Upper Midwest Environmental Sciences Center is developing deep learning algorithms and tools for the automatic detection, enumeration, classification, and annotation of seabirds and other marine wildlife from digital aerial imagery — advancing cutting-edge research in collaboration with the Bureau of Ocean Energy Management (BOEM), the U.S. Fish and Wildlife Service (FWS...
Learn More

Code, imagery, and annotations for training a deep learning model to detect wildlife in aerial imagery

There are 3 child zip files included in this data release. 01_Codebase.zip contains a codebase for using deep learning to filter images based on the probability of any bird occurrence. It includes instructions and files necessary for training, validating, and testing a machine learning detection algorithm. 02_Imagery.zip contains imagery that were collected using a Partenavia P68 fixed-wing airpla
By
Upper Midwest Environmental Sciences Center

Images and annotations to automate the classification of avian species

This dataset is a collection of cropped avian images that pair with species identification annotation values.
By
Upper Midwest Environmental Sciences Center

Aerial thermal imagery of the Central Platte River Valley and bounding box annotations of sandhill cranes

Aerial thermal imagery was collected over the Central Platte River Valley, Nebraska, USA. Bounding box annotations were manually created for the purpose of machine learning tasks to automate the detection of sandhill cranes. Mosaicking of the thermal imagery was complete to assemble the individual images into a single, geo-referenced image.
By
Upper Midwest Environmental Sciences Center

Deep learning workflow to support in-flight processing of digital aerial imagery for wildlife population surveys

Deep learning shows promise for automating detection and classification of wildlife from digital aerial imagery to support cost-efficient remote sensing solutions for wildlife population monitoring. To support in-flight orthorectification and machine learning processing to detect and classify wildlife from imagery in near real-time, we evaluated deep learning methods that address hardware limitati
Authors
Tsung-Wei Ke, Stella X Yu, Mark D. Koneff, David L. Fronczak, Luke J. Fara, Travis Harrison, Kyle Lawrence Landolt, Enrika Hlavacek, Brian R. Lubinski, Timothy White
By
Ecosystems Mission Area, Upper Midwest Environmental Sciences Center

Challenges and solutions for automated avian recognition in aerial imagery

Remote aerial sensing provides a non-invasive, large geographical-scale technology for avian monitoring, but the manual processing of images limits its development and applications. Artificial Intelligence (AI) methods can be used to mitigate this manual image processing requirement. The implementation of AI methods, however, has several challenges: (1) imbalanced (i.e., long-tailed) data distribu
Authors
Zhonqgi Miao, Stella X Yu, Kyle Lawrence Landolt, Mark D. Koneff, Timothy White, Luke J. Fara, Enrika Hlavacek, Bradley A. Pickens, Travis J. Harrison, Wayne M. Getz
By
Ecosystems Mission Area, Upper Midwest Environmental Sciences Center

Automating sandhill crane counts from nocturnal thermal aerial imagery using deep learning

Population monitoring is essential to management and conservation efforts for migratory birds, but traditional low-altitude aerial surveys with human observers are plagued by individual observer bias and risk to flight crews. Aerial surveys that use remote sensing can reduce bias and risk, but manual counting of wildlife in imagery is laborious and may be cost-prohibitive. Therefore, automated met
Authors
Emilio Luz-Ricca, Kyle Lawrence Landolt, Bradley A. Pickens, Mark D. Koneff
By
Ecosystems Mission Area, Upper Midwest Environmental Sciences Center

Science and Products

link
An aerial image of a flock of waterfowl floating on the ocean.

Deep Learning for Automated Detection and Classification of Waterfowl, Seabirds, and other Wildlife from Digital Aerial Imagery

The U.S. Geological Survey Upper Midwest Environmental Sciences Center is developing deep learning algorithms and tools for the automatic detection, enumeration, classification, and annotation of seabirds and other marine wildlife from digital aerial imagery — advancing cutting-edge research in collaboration with the Bureau of Ocean Energy Management (BOEM), the U.S. Fish and Wildlife Service (FWS...
By
Ecosystems Mission Area, Species Management Research Program, Upper Midwest Environmental Sciences Center
link

Deep Learning for Automated Detection and Classification of Waterfowl, Seabirds, and other Wildlife from Digital Aerial Imagery

The U.S. Geological Survey Upper Midwest Environmental Sciences Center is developing deep learning algorithms and tools for the automatic detection, enumeration, classification, and annotation of seabirds and other marine wildlife from digital aerial imagery — advancing cutting-edge research in collaboration with the Bureau of Ocean Energy Management (BOEM), the U.S. Fish and Wildlife Service (FWS...
Learn More

Code, imagery, and annotations for training a deep learning model to detect wildlife in aerial imagery

There are 3 child zip files included in this data release. 01_Codebase.zip contains a codebase for using deep learning to filter images based on the probability of any bird occurrence. It includes instructions and files necessary for training, validating, and testing a machine learning detection algorithm. 02_Imagery.zip contains imagery that were collected using a Partenavia P68 fixed-wing airpla
By
Upper Midwest Environmental Sciences Center

Images and annotations to automate the classification of avian species

This dataset is a collection of cropped avian images that pair with species identification annotation values.
By
Upper Midwest Environmental Sciences Center

Aerial thermal imagery of the Central Platte River Valley and bounding box annotations of sandhill cranes

Aerial thermal imagery was collected over the Central Platte River Valley, Nebraska, USA. Bounding box annotations were manually created for the purpose of machine learning tasks to automate the detection of sandhill cranes. Mosaicking of the thermal imagery was complete to assemble the individual images into a single, geo-referenced image.
By
Upper Midwest Environmental Sciences Center

Deep learning workflow to support in-flight processing of digital aerial imagery for wildlife population surveys

Deep learning shows promise for automating detection and classification of wildlife from digital aerial imagery to support cost-efficient remote sensing solutions for wildlife population monitoring. To support in-flight orthorectification and machine learning processing to detect and classify wildlife from imagery in near real-time, we evaluated deep learning methods that address hardware limitati
Authors
Tsung-Wei Ke, Stella X Yu, Mark D. Koneff, David L. Fronczak, Luke J. Fara, Travis Harrison, Kyle Lawrence Landolt, Enrika Hlavacek, Brian R. Lubinski, Timothy White
By
Ecosystems Mission Area, Upper Midwest Environmental Sciences Center

Challenges and solutions for automated avian recognition in aerial imagery

Remote aerial sensing provides a non-invasive, large geographical-scale technology for avian monitoring, but the manual processing of images limits its development and applications. Artificial Intelligence (AI) methods can be used to mitigate this manual image processing requirement. The implementation of AI methods, however, has several challenges: (1) imbalanced (i.e., long-tailed) data distribu
Authors
Zhonqgi Miao, Stella X Yu, Kyle Lawrence Landolt, Mark D. Koneff, Timothy White, Luke J. Fara, Enrika Hlavacek, Bradley A. Pickens, Travis J. Harrison, Wayne M. Getz
By
Ecosystems Mission Area, Upper Midwest Environmental Sciences Center

Automating sandhill crane counts from nocturnal thermal aerial imagery using deep learning

Population monitoring is essential to management and conservation efforts for migratory birds, but traditional low-altitude aerial surveys with human observers are plagued by individual observer bias and risk to flight crews. Aerial surveys that use remote sensing can reduce bias and risk, but manual counting of wildlife in imagery is laborious and may be cost-prohibitive. Therefore, automated met
Authors
Emilio Luz-Ricca, Kyle Lawrence Landolt, Bradley A. Pickens, Mark D. Koneff
By
Ecosystems Mission Area, Upper Midwest Environmental Sciences Center
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