東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Every Field in Minnesota: Building a...

Bakker, Jesse.

Linked to FindBook

Google Book

Amazon

博客來

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

Record Type:	Electronic resources : Monograph/item
Title/Author:	Every Field in Minnesota: Building a Geographically Scalable Satellite Imagery Analytics System for Mapping Crop Fields./
Author:	Bakker, Jesse.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	111 p.
Notes:	Source: Masters Abstracts International, Volume: 82-05.
Contained By:	Masters Abstracts International82-05.
Subject:	Geography. -
Online resource:	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.
LDR:02361nmm a2200349 4500 001 2279761
005 20210823083438.5
008 220723s2020 ||||||||||||||||| ||eng d
020 $a 9798678180834
035 $a (MiAaPQ)AAI28093752
035 $a AAI28093752
040 $a MiAaPQ $c MiAaPQ
100 1 $a Bakker, Jesse. $3 3558234
245 1 0 $a Every Field in Minnesota: Building a Geographically Scalable Satellite Imagery Analytics System for Mapping Crop Fields.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 111 p.
500 $a Source: Masters Abstracts International, Volume: 82-05.
500 $a Advisor: Shook, Eric.
502 $a Thesis (M.A.)--University of Minnesota, 2020.
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.
590 $a School code: 0130.
650 4 $a Geography. $3 524010
650 4 $a Geological engineering. $3 2122713
650 4 $a Remote sensing. $3 535394
653 $a Satellite imagery
653 $a Analytics system
653 $a Crop field
690 $a 0366
690 $a 0466
690 $a 0799
710 2 $a University of Minnesota. $b Geography. $3 1028888
773 0 $t Masters Abstracts International $g 82-05.
790 $a 0130
791 $a M.A.
792 $a 2020
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28093752