Abstract中国属于发展中国家。截止2017年，中国乡村常驻人口为5.77亿人，占全国总人口的41.48%。2018年，党的十九大胜利召开，提出了全面建成小康社会和乡村振兴发展战略，中国农村正处于转型重构的重大发展时期。习总书记强调，全面建成小康社会最艰巨、最繁重的任务在农村，没有农村的小康，特

随着生活水平提高，公众的健康需求和环境意识也日益增长，关注点已经从天气、温度等气象数据，拓展到空气质量等环境数据。大气环境大数据在公共领域的应用，能够展现可视化的区域环境质量，及其动态变化过程。本系统是对地理信息系统开发课的一次综合实战演练。系统主要实现对获得的空气质量数据进行时空插值、异常检测和可

摘要针对高分辨率遥感影像中地物的复杂性和多变性带来的地物提取难点，提出了一种基于多层次规则的面向对象的典型地物提取方法。改进了基于区域增长的影像分割方法，利用小区域内的全局最优策略进行初始增长，避开了种子点的选择。利用影像分割得到的影像对象作为地物提取的基元，针对影像上典型地物选择提取特征 ，利用多

登记号：2020SR1759753下载地址GeoCA v2.4: Geographical Simulation App via Pixel-based Cellular Automata

Awareness is mounting that urban greenspace is beneficial for residents’ health. While a plethora of studies have focused on greenspace quantity, scant attention has been paid to greenspace quality. Existing methods for assessing greenspace quality is either highly labor-intensive and/or prohibitively time-consuming. This study develops a new machine learning method to assess greenspace quality based on street view images collected from Guangzhou, China. It also examines whether greenspace exposure disparities are linked to the neighbourhood socioeconomic status (SES). The validation process indicated that our scoring system achieved high accuracy for predicting street view-based greenspace quality outside the training data. Results also show that there were marked differences in spatial distribution between aggregated NDVI (Normalized Difference Vegetation Index), street view greenness quantity and quality. Regression models show that neighbourhood SES is not associated with NDVI. Although neighbourhood SES is associated with both street view greenness quantity and quality index value, street view greenness quality is more sensitive to the change of neighbourhood SES. Our work suggests that policymakers and planners are advised to pay more attention to greenspace quality and greenspace exposure disparities in urban area.