Landsat-based Global Urban Area Map


IDENTIFICATION INFORMATION

Name Landsat-based Global Urban Area Map
Abbreviation LaGURAM
Metadata Identifier LaGURAM20181216213158-DIAS20180903143952-en

CONTACT

CONTACT on DATASET

Name Hiroyuki Miyazaki
Organization University of Tokyo
Address Institute of Industrial Science, 4-6-1 Komaba, Meguro, Tokyo, 153-8505, Japan
TEL +81-3-5452-6415
FAX +81-3-5452-6412
E-mail heromiya@csis.u-tokyo.ac.jp

CONTACT on PROJECT

Data Integration and Analysis System

Name DIAS Office
Organization Remote Sensing Technology Center of Japan
Address TOKYU REIT Toranomon Building 2F 3-17-1 Toranomon, Minato-ku, Tokyo, 105-0001, Japan
E-mail dias-office@diasjp.net

DOCUMENT AUTHOR

Name Hiroyuki Miyazaki
Organization University of Tokyo
E-mail heromiya@csis.u-tokyo.ac.jp

DATASET CREATOR

Name Hiroyuki Miyazaki
Organization University of Tokyo
E-mail heromiya@csis.u-tokyo.ac.jp

DATE OF THIS DOCUMENT

2018-12-16

DATE OF DATASET

  • creation : 2014-12-01

DATASET OVERVIEW

Abstract

The Landsat-based Global Urban Area Map (LaGURAM) is a dataset of urban/non-urban classification map developed from time-series Landsat data provided by US Geological Survey. In the dataset, "urban" is defined with existence of built-up areas and pavement, a physical aspect of urban areas. The data is developed primarily for 1990, 2000, 2005, and 2010 although the target year can be flexible to users' request. The data has been initially developed for major cities of the world. The data will be improved in accuracy especially for regions of interest requested by end users. Please contact with the author if you have any interest in the LaGURAM dataset.

Topic Category(ISO19139)

  • economy

  • planningCadastre

  • structure

Temporal Extent

Begin Date 1990-01-01
End Date 2010-12-31

Geographic Bounding Box

North bound latitude 90
West bound longitude -180
Eastbound longitude 180
South bound latitude -90

Grid

Dimension Name Dimension Size (slice number of the dimension) Resolution Unit
row 1 30 (m)
column 1 30 (m)

Keywords

Keywords on Dataset

Keyword Type Keyword Keyword thesaurus Name
theme Disasters, Energy GEOSS

Keywords on Project

Data Integration and Analysis System
Keyword Type Keyword Keyword thesaurus Name
theme DIAS > Data Integration and Analysis System No_Dictionary

DATA PROCESSING

Data Processing (1)

General Explanation of the data producer's knowledge about the lineage of a dataset

1. Method

The algorithm of the urban extent mapping was developed for Landsat TM and ETM+ data based on a machine-learning-based algorithm named Learning with Local and Global Consistency (LLGC) with improvements for remote sensing data . Basic concept of the algorithm is as the following: (i) initiate urban extent data with course scale urban extent map; (ii) overlay Landsat data on the initial urban extent data; (iii) classify Landsat pixels based on initial urban extent data and proximity between pixel values of TM or ETM+ bands; (iv) iterate the classification from (i) to (iii). The algorithm yields confidence of existence of urban development at each pixel with a range between 0 and 1. The urban extent map was generated by thresholding the confidence at 0.5. For some cities with more interest, confidence value was calculated from median values of four scenes of Landsat data for better robustness.

2. Input Data

Landsat TM and ETM+: The data was acquired primarily from public archive of cloud-free Landsat data operated by Global Land Cover Facility, University of Maryland , and supplementarily from Landsat data archive operated by US Geological Survey (USGS). The method was applied to Landsat data selected for the cities with more than one million population for 1990, 2000, 2005, and 2010. The coverage of Landsat data was 5200 scenes of WRS tiles.

Initial urban extent data: MCD12Q1 , a global land cover dataset with 500-m resolution developed from MODIS satellite data was used in the algorithm as the initial urban extent data.

Hydrology data: As the coarse resolution land cover maps is not likely to recognize major rivers in urbanized areas, such pixels needed to be excluded from initial urban extent data. USGS’s HydroSHEDS was used to identify major rivers in urbanized areas for better result of the classification.

Point coordinates of target cities: Global Rural-Urban Mapping Project (GRUMP) Settlement Points developed by Center for International Earth Science Information Network (CIESIN) was used to identify location of target cities with more than one million population. List of cities with more than one million population and those latitude/longitude coordinates was extracted from the dataset by thresholding estimated population for 2000.

3. Result

The algorithm was applied to the input data and generated urban extent data for 1990, 2000, 2005, and 2010. For better usefulness of the output data, the data was organized into Google Map’s Tile Mapping System with zoom level of 10. Size of each tile is approximately 40 km x 40 km. Number of tiles is 22,217 for the target coverage.

Quality of the data was assessed by kappa coefficient with initial urban extent data and also visually assessed for major cities of the world, including the data used for the exhibition “Evolution of Risk” at the Third UN World Conference on Disaster Risk Reduction.

USE CONSTRAINTS

Data Policy for Project

Data Integration and Analysis System

The terms of data use of data providers take first priority over the DIAS data usage policy. In the event a data provider has not established terms of use, the following DIAS project data terms of use apply.

1. Users shall prioritize and abide by terms of use stipulated by a data provider in the event such exist

2. The use of DIAS data sets is limited to research and educational purposes [*1]

3. Users shall not modify the content of DIAS data sets

4. Users shall not provide the content of DIAS data sets to third parties

5. In the event of using DIAS data sets in an academic presentation, paper, article, or report, etc., users shall cite in parenthesis the text given as the data citation

6. In the event of using DIAS data sets in an academic presentation, paper, article, or report, etc., users shall submit a copy of the work (an offprint in the case of a paper, or a copy of the lecture summary in the case of an oral or poster presentation) to the DIAS office below

[*1] Data sets whose commercial usage are allowed under the data policy by data provider will be also allowed to be used commercially as DIAS data sets, after ongoing preparation works have been completed. Please contact the DIAS Office for more details.

[DIAS Office]

E-mail: dias-office@diasjp.net

Remote Sensing Technology Center of Japan

TOKYU REIT Toranomon Building 2F 3-17-1 Toranomon, Minato-ku, Tokyo, 105-0001

Disclaimer for Project

Data Integration and Analysis System

1. DIAS data provider is not liable for any losses or any damage when DIAS data sets are used.

2. DIAS data and related information are subject to change without any prior notice.

3. DIAS data sets provided are not supported for any additional processing or analysis.

ACKNOWLEDGEMENT

Dataset Acknowledgement

This data was developed with supports by Global Facility for Disaster Reduction and Recovery, the World Bank, and Data Integration and Analysis System (DIAS), the University of Tokyo.

Project Acknowledgement

Data Integration and Analysis System

Whenever DIAS dataset is used for any academic presentations, and any publication of scientific results, the author(s) shall specify the following acknowledgement and if the data provider has their own acknowledgement quotation, the author(s) shall use both acknowledgements.

"The DIAS dataset is archived and provided under the framework of the Data Integration and Analysis System (DIAS) funded by Ministry of Education, Culture, Sports, Science and Technology (MEXT)."

REFERENCES

Miyazaki, H., X. Shao, K. Iwao and R. Shibasaki (2013). "An automated method for global urban area mapping by integrating ASTER satellite images and GIS data." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 6(2): 1-27.


Copyright(c) 2006-2018 Data Integration & Analysis System (DIAS) All Rights Reserved.
This project is supported by "Data Integration & Analysis System" funded by MEXT, Japan
Copyright © 2009-2019 DIAS All Rights Reserved.