CONTEXTS.py (CS.py):一种监督的上下文后分类方法,用于访问复杂的地理空间对象的多维维度
Vincenza Ferrara1,2, Johan Lindberg3, Anders Wästfelt2
1Department of Archaeology and Ancient History, Uppsala University - Engelska Parken, Thunbergsvägen 3H, Uppsala 751 26, Sweden.
MethodsX
|May 29, 2024
概括
一个新的QGIS插件CONTEXTS.py (CS.py) 通过分析地球图像中的概念空间及其上下文关系来提取复杂的空间对象. 这增强了地理空间分析与土地使用的定性维度.
科学领域:
- 地理信息系统 (GIS) 是指地理信息系统.
- 遥感 遥感 遥感 遥感
- 空间分析 空间分析
背景情况:
- 空间特征的质量维度在传统分析中经常被忽视.
- 现有的方法很难从单个地球图像中提取语义上复杂的空间对象.
- 概念空间,代表空间配置的心理模型,提供了一个新的框架.
研究的目的:
- 提出一种新的监督后分类方法,用于提取语义上复杂的空间对象.
- 介绍CONTEXTS.py (CS.py),这是一个QGIS插件,可以操作这种方法.
- 为了证明插件在识别多样化的土地使用概念空间方面的能力.
主要方法:
- 使用由用户定义的培训领域的监督后分类方法.
- 通过CONTEXTS.py (CS.py) 进行操作,这是QGIS.py的Python插件.
- 基于匹配空间特征和背景的概念空间的识别.
主要成果:
- 在西西里,CS.py成功地从一个正方形照片中检测出了土地利用的各种概念空间.
- 该插件在通常被归类为单一土地覆盖的区域内识别了多个土地使用维度 (时间,文化,社会).
- 该方法将地理空间分析能力扩展到定性信息.
结论:
- CS.py提供了一个简单,实用和可访问的工具,用于监督的上下文后分类.
- 它将上下文信息集成到空间分类标准中,补充了现有的基于对象的工具.
- 该插件从定量和定性角度对景观调查有广泛的应用.
相关概念视频
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