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Every Field in Minnesota: Building a...

Bakker, Jesse.

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Every Field in Minnesota: Building a Geographically Scalable Satellite Imagery Analytics System for Mapping Crop Fields.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Every Field in Minnesota: Building a Geographically Scalable Satellite Imagery Analytics System for Mapping Crop Fields./
作者:	Bakker, Jesse.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	111 p.
附註:	Source: Masters Abstracts International, Volume: 82-05.
Contained By:	Masters Abstracts International82-05.
標題:	Geography. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28093752
ISBN:	9798678180834

Every Field in Minnesota: Building a Geographically Scalable Satellite Imagery Analytics System for Mapping Crop Fields.
Bakker, Jesse.

Every Field in Minnesota: Building a Geographically Scalable Satellite Imagery Analytics System for Mapping Crop Fields. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 111 p.

Source: Masters Abstracts International, Volume: 82-05.

Thesis (M.A.)--University of Minnesota, 2020.

This item must not be sold to any third party vendors.

Remote sensing is a common tool in agriculture for crop classification and monitoring. Globally available high resolution imagery makes it possible to delineate individual field boundaries, which can serve as a foundational data set for secondary agricultural analysis. For the most accurate results, the methods employed in field classification and delineation are often fine-tuned to the agricultural conditions within the local geographic context of interest. This fine-tuning, however, can make it difficult to implement the same models in other locations to the same degree of accuracy. While these locally-tuned examples provide valuable insight into developing crop classification systems, a classification model that is applicable at larger spatial scales (e.g. national, global) requires a different approach. This paper proposes a geographically scalable workflow that integrates unsupervised machine learning, local knowledge, and emerging deep learning techniques to enable accurate, flexible field delineation. This approach achieved 88.1% overall accuracy in classifying agricultural fields for the state of Minnesota without relying on any external training data. This workflow can serve as a prototype for globally scalable field mapping.

ISBN: 9798678180834Subjects--Topical Terms:

524010
Geography.
Subjects--Index Terms:

Satellite imagery

Every Field in Minnesota: Building a Geographically Scalable Satellite Imagery Analytics System for Mapping Crop Fields.
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500 $a Source: Masters Abstracts International, Volume: 82-05.
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506 $a This item must not be sold to any third party vendors.
520 $a Remote sensing is a common tool in agriculture for crop classification and monitoring. Globally available high resolution imagery makes it possible to delineate individual field boundaries, which can serve as a foundational data set for secondary agricultural analysis. For the most accurate results, the methods employed in field classification and delineation are often fine-tuned to the agricultural conditions within the local geographic context of interest. This fine-tuning, however, can make it difficult to implement the same models in other locations to the same degree of accuracy. While these locally-tuned examples provide valuable insight into developing crop classification systems, a classification model that is applicable at larger spatial scales (e.g. national, global) requires a different approach. This paper proposes a geographically scalable workflow that integrates unsupervised machine learning, local knowledge, and emerging deep learning techniques to enable accurate, flexible field delineation. This approach achieved 88.1% overall accuracy in classifying agricultural fields for the state of Minnesota without relying on any external training data. This workflow can serve as a prototype for globally scalable field mapping.
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653 $a Satellite imagery
653 $a Analytics system
653 $a Crop field
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