Understanding the hydrological valley landscape: A multi-scenario adaptive framework for delineating valley floors
May 7, 2025
Characterizing spatial patterns and regionalization of anthropogenic landforms using multi-source geospatial data: Insights from Loess Plateau of China
Anthropogenic forces have become a significant factor in the development of geomorphology, influencing landform morphology and usage in diverse ways across natural and societal environments. Despite their growing impact, few studies have quantified the morphology and distribution of anthropogenic landforms at large scales to reveal the spatial patterns of human modification on the surface. To address them, we propose a framework that includes three classical anthropogenic landforms, i.e., terraces, check dams, and impervious surfaces, on the Chinese Loess Plateau (CLP) to measure the morphology differences at the basin level. This research provides a new perspective and robust methodological framework for analyzing the morphology pattern of anthropogenic landforms. It could be a data foundation for future studies exploring the interactions between human activities and geomorphological processes.
Mar 7, 2025
Deep learning detects entire multiple-size lunar craters driven by elevation data and topographic knowledge
Lunar craters are important geomorphological features, that provide valuable insights into lunar morphology, geology, and impact processes. However, the current understanding of lunar craters of different sizes, especially smaller craters (diameter <5 km), is still incomplete. The lack of understanding of small lunar craters affects our understanding of the lunar surface and its geological history. Therefore, in this study, we propose a deep learning Crater Detection Algorithms (CDA), called Lunar Topographic Knowledge Attention U-Net (LTKAU-Net) that integrates a Digital Elevation Model (DEM) and topographic knowledge.
Feb 7, 2025
Delineating individual alluvial fans and morphological analysis based on digital elevation models
We proposed a novel method for delineating individual alluvial fans based on free digital elevation models. This method incorporated multi-directional hillshades to accurately extract alluvial slopes, and two flow direction algorithms to support the delineation of individual fans. The method was tested in Death Valley, where a total of 89 individual fans were delineated for morphological analysis.
Jan 27, 2025
Assessing the influence of geo-environmental factors on discontinuous gully erosion at regional scales: A case study of spoon gullies on the loess plateau of China
Spoon gullies, characterized by fat heads and thin tails, are a typical type of discontinuous hillslope gully found extensively on the Loess Plateau of China. This study explores the variations in factor dominance influencing the gully erosion within a global-local framework, using Geodetector, geographically weighted regression, and spatially constrained multivariate clustering. The study provides new insights into the spatial heterogeneity of discontinuous gullies at a regional scale and offers implications for developing targeted strategies against gully erosion.
Dec 31, 2024
Is 3D building morphology really related to land surface temperature? Insights from a new homogeneous unit
This study places special emphasis on proposing a new spatial unit, the Homogenous Unit of Building Morphology (HUBM), to re-describe building morphology and re-analyze its effect on LST with less uncertainty.
Dec 1, 2024
DEM-based sand dune analysis: Identification and morphological regionalization in the Grand Erg Oriental
Sand dunes are predominantly distributed in arid regions. Automatic mapping and regionalization of sand dunes in large-scale areas are crucial to understanding the evolution trends of aeolian sand environments. Different from existing studies primarily focused on mapping the extent of desert areas, this study proposes a framework for automatic identification and comprehensive regionalization based on dunes morphology.
Nov 28, 2024
Refining urban morphology: An explainable machine learning method for estimating footprint-level building height
This study bridged this gap by using random forest models to integrate the elevation, geometry and shape attributes of individual buildings, further refining those with spatial aggregation.
Oct 1, 2024
A new hierarchical analysis framework of building heights: Towards a more intuitive understanding of 3D urban structure
This article proposes a new framework to uncover the 3D urban structure. Firstly, kernel density is employed to reveal the hierarchical spatial structure of buildings and the contour tree method is improved to quantitatively measure the spatial diversity and complexity. Then, the 3D urban structure is abstracted by spatial interpolation after feature filtration. Finally, this framework is applied to the central area of Chengdu City.
Dec 28, 2023
An Automatic Approach to Extracting Large-Scale Three-Dimensional Road Networks Using Open-Source Data
We proposed a novel approach to extract large-scale 3D road networks, integrating terrain correction and road engineering rule constraint, by using the Advanced Land Observing Satellite World 3D-30 m DSM, OpenStreetMap and FABDEM.
Nov 14, 2022