<b>Unsupervised land-use change detection using </b><b>multi-temporal</b><b> POI embedding</b>
Creators: name: Yao, Yao
name: Zhu, Qia
name: Huang, Weiming
name: Zijin, Guo
name: Zhang, Yatao
name: Yan, Xiaoqin
name: Dong, Anning
name: Jiang, Zhangwei
name: Liu, Hong
name: Guan, Qingfeng
Publisher: name: figshare
Year: 2023
Subjects: classificationCode: 370499
subject: Geoinformatics not elsewhere classified

description: <b>The MT-POI2Vec model</b> <b>Content</b>This project is linked to a submission to International Journal of Geographical Information Science, entitled “<b>Unsupervised land-use</b> <b>change detection using multi-temporal POI embedding</b>”, which proposes a multi-temporal POI embedding (MT-POI2Vec) technique for land use change detection in a fully unsupervised manner.The project includes both the data and codes that support the MT-POI2Vec model.ContentIn the folder 'Data', there are four files:<b>An original file:</b>1) poi_integrate.csv: This file is the sub-sampled integrated POI data used in this study, and an overlap operation has been done to indicate which region a particular POI resides in. The column named 'Year' distinguish the year of POIs from 2013 and 2020.<b>Three processed files:</b>2) edge_index_weight_2013.csv: This file stores the edge information of the Delaunay triangle network constructed by the POIs from 2013, containing two columns, namely 'source' and 'target' respectively. The two columns contain the indexes of nodes in every triangle.3) edge_index_weight_2020.csv: This file stores the edge information of the Delaunay triangle network constructed by the POIs from 2020, containing two columns, namely 'source' and 'target' respectively. The two columns contain the indexes of nodes in every triangle.4) shenzhen_edge_index_weight.csv: This file stores the edge information of the Delaunay triangle constructed by the POIs from 2013 and 2020, containing three columns, namely 'source' , 'target' and 'weight' respectively. The column 'weight' contains the product of two prepared transition biases.In the folder 'checkpoint', there is only a file:1) improved_semantic_embedding_l10_l7.pth: This file stores the trained optimal embeddings of second-level categories of POIs from two years.This project contains 5 python scripts totally. The core of the program lies in the python script:1) poi_embedding.py: This python script learns comprehensive POI embeddings (second-level categories) using the proposed approach, coupling with the spatial co-occurrence relations of POIs and the categorical semantic information. This script is linked to two other scripts:2) Variables.py: This python script stores the hyper parameters used in the learning process; detailed annotations can be found in the script.3) poi_network.py: This python script computes the product of all three transition biases, then performs spatially-explicit random walks on the Delaunay triangle networks, and match the within-year and cross-year second-level categorical semantics of POIs. In addition, this python srcipt receives an input parameters denotes the path of the csv file storing edge index and pre-assigned two transition biases. This csv file is generated by the remaining two python scripts together:<br>i) DelaunayTriangulation.py: This python script constructs the Delaunay triangle networks from multi-temporal POIs respectively and save them in a tabular form, containing two columns namely 'source' and 'target' respectively.ii) compute_transition.py: This python script is used to compute two pre-assigned transition biases, which will be applied in the process of spatially explicit random walk in the poi_network.py. In this python script, we re-implemented and enriched the procedures of assigning transition biases in Huang et al. (2022) from the repository https://github.com/RightBank/Semantics-preserved-POI-embedding.The environment required for this project includes:Python 3.7; Pytorch; Stellargraph; Pandas; sklearn; Scipy; Pyproj 3.2.1