Scene Classification Based on the Fully Sparse Semantic Topic Model

AbstractIn high spatial resolution (HSR) imagery scene classification, it is a challenging task to recognize the high-level semantics from a large vol

基于“反规划”理念及FLUS模型的城镇用地增长边界划定研究

Abstract城镇用地增长边界划定对城镇用地扩张调控与管理具有重要意义，但当前仍无统一划定思路与方法。基于“反规划”理念，引入FLUS模型探讨城镇用地增长边界划定思路，并以徐州市贾汪区为例，采用2009年和2014年数据对FLUS模型可靠性进行验证，对2020年研究区土地利用变化进行情景模拟，在此

基于SD模型的中国2010—2050年土地利用变化情景模拟

摘要通过将中国划分为4个生态区，在综合考虑社会经济和自然因素的前提下，利用系统动力学的原理和方法，选取对土地利用变化影响最大的驱动因素，分区构建中国土地利用变化系统动力学模型并模拟4种发展情景下2050年中国土地利用变化情况。结果表明：在不同情景设定下，土地利用变化差异较大，其中平稳发展情景较理想，

Simulating land-use changes by integrating dynamic land parcel subdivision and vector-based CA

AbstractCellular automata (CA) have been widely used to simulate complex urban development processes. Previous studies indicated that vector-based cel

基于多窗口线性回归模型的浙北地区冬季气温估算

摘要气温（T）是描述陆地气候环境的一个重要参数，其异常变化直接影响人类的生存环境，因此如何高精度地估算气温成为当前研究的热点。MODIS数据因其分辨率较低不能提供精细的地表信息，为此，本文以更高分辨率的Landsat-8影像为数据源，结合自动气象站的气温数据，耦合经纬度、归一化植被指数、归一化建筑指

Classifying urban land use by integrating remote sensing and social media data

AbstractUrban land use information plays an important role in urban management, government policy-making, and population activity monitoring. However,

A New Global Land-Use and Land-Cover Change Product at a 1-km Resolution for 2010 to 2100

AbstractGlobal land-use and land-cover change (LUCC) data are crucial for modeling a wide range of environmental conditions. So far, access to high-re

Assessing the impacts of sprawl on net primary productivity using fusion of Landsat and MODIS data

AbstractUrbanization has profoundly altered the terrestrial ecosystem carbon cycle, especially the net primary productivity (NPP). Many attempts have

耦合卡尔曼滤波和多层次聚类的中国PM2.5时空分布分析

AbstractSerious air pollution has recently aroused wide public concerns in China. The traditional method of quantitative remote sensing model is not o

Mapping population distributions at the building level by integrating multisource geospatial data

Fine-scale population distribution data at the building level play an essential role in numerous fields, for example urban planning and disaster prevention. The rapid technological development of remote sensing (RS) and geographical information system (GIS) in recent decades has benefited numerous population distribution mapping studies. However, most of these studies focused on global population and environmental changes; few considered fine-scale population mapping at the local scale, largely because of a lack of reliable data and models. As geospatial big data booms, Internet-collected volunteered geographic information (VGI) can now be used to solve this problem. This article establishes a novel framework to map urban population distributions at the building scale by integrating multisource geospatial big data, which is essential for the fine-scale mapping of population distributions. First, Baidu points-of-interest (POIs) and real-time Tencent user densities (RTUD) are analyzed by using a random forest algorithm to down-scale the street-level population distribution to the grid level. Then, we design an effective iterative building-population gravity model to map population distributions at the building level. Meanwhile, we introduce a densely inhabited index (DII), generated by the proposed gravity model, which can be used to estimate the degree of residential crowding. According to a comparison with official community-level census data and the results of previous population mapping methods, our method exhibits the best accuracy (Pearson R = .8615, RMSE = 663.3250, p < .0001). The produced fine-scale population map can offer a more thorough understanding of inner city population distributions, which can thus help policy makers optimize the allocation of resources.