Geospatial Data and Google Street View Images for Monitoring Kudzu Vines in Small and Dispersed Areas
Comprehensive reviews of continuously vegetated areas to determine dispersed locations of invasive species require intensive use of computational resources. Furthermore, effective mechanisms aiding identification of locations of specific invasive species require approaches relying on geospatial indi...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Earth |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4834/6/2/40 |
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| Summary: | Comprehensive reviews of continuously vegetated areas to determine dispersed locations of invasive species require intensive use of computational resources. Furthermore, effective mechanisms aiding identification of locations of specific invasive species require approaches relying on geospatial indicators and ancillary images. This study develops a two-stage data workflow for the invasive species Kudzu vine (<i>Pueraria montana</i>) often found in small areas along roadsides. The INHABIT database from the United States Geological Survey (USGS) provided geospatial data of Kudzu vines and Google Street View (GSV) a set of images. Stage one built up a set of Kudzu images to be implemented in an object detection technique, You Only Look Once (YOLO v8s), for training, validating, and testing. Stage two defined a dataset of confirmed locations of Kudzu which was followed to retrieve images from GSV and analyzed with YOLO v8s. The effectiveness of the YOLO v8s model was assessed to determine the locations of Kudzu identified from georeferenced GSV images. This data workflow demonstrated that field observations can be virtually conducted by integrating geospatial data and GSV images; however, its potential is confined to the updated periodicity of GSV images or similar services. |
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| ISSN: | 2673-4834 |