Myanmar Information Management Unit (MIMU)

MIMU Pcode is similar to a zip code or postal code, and is a part of a data management system providing unique reference codes to around 67,000 locations across Myanmar. The MIMU maintains the P-codes for Myanmar based on information collected from a variety of sources, including the Government gazette and humanitarian and development organizations. Without a system for organizing such data it is almost impossible for data from more than one source to be combined.

Land use in Myanmar from UNEP 2000 data.

Myanmar national river network (polylines). Place names from General Administration Department (GAD) with transliteration by MIMU. Polylines by MIMU based on the Digital Chart of the World. This dataset was mainly digitized at 1:1,000,000 scale. Place names are in line with the general cartographic practice to reflect the names of such places as designated by the government concerned. The United Nations and the MIMU specifically do not make any warranties or representations as to the accuracy or completeness of this dataset nor does it imply official endorsement or acceptance by the United Nations.

Myanmar national river network(lines). Data from the Digital Chart of the World. Digitization revised by MIMU at 1:250,000 scale. This dataset was mainly digitized at 1:250’000 scale. Place names are in line with the general cartographic practice to reflect the names of such places as designated by the government concerned.

Geotiff download link : http://bit.ly/2VOvrlD Metadata Download link: http://geonode.themimu.info/documents/27 A Landsat-based classification of forest cover in Kachin State and Sagaing Region with a focus on changes in intact forest between 2002 and 2014. This dataset expands upon the Myanmar 2002-2014 Forest Cover Change raster*, by differentiating mining from other non-forest land cover classes. Developed by: ALARM, Smithsonian Institution, GMAP and American Museum of Natural History *http://geonode.themimu.info/layers/geonode%3Amyanmar_forestcoverchange See the recommended style file (.txt) in the documents section for associating raster values and creating the legend. Original dataset title: Kachin State and Sagaing Region 2002-2014 Forest Cover Change

Geotiff download link : http://bit.ly/2wq7KFy Metadata download link: http://geonode.themimu.info/documents/26 A Landsat-based classification of forest cover in Tanintharyi Region with a focus on changes in intact forest between 2002 and 2014. This dataset expands upon the Myanmar 2002-2014 Forest Cover Change raster*, by differentiating oil palm plantations from other plantations. Developed by: ALARM, Smithsonian Institution, GMAP and American Museum of Natural History *http://geonode.themimu.info/layers/geonode%3Amyanmar_forestcoverchange See the recommended style file (.txt) in the documents section for associating raster values and creating the legend. Original dataset title: Tanintharyi Region 2002-2014 Forest Cover Change

Geotiff download link : http://bit.ly/2PHtJyf This is a Landsat 8-based classification of the extent and condition of mangrove forests in Tanintharyi Region. The classification is derived from satellite images which were acquired in 2014. The classification is based on ground truth data, which was collected in May and June 2015. In addition, further training polygons were digitized based on high-resolution Google Maps and Bing Maps data. This dataset is a filtered version of the initial classification result. The classified mangrove classes are ‘Intact to slightly degraded mangroves’, ‘Degraded mangroves’, ‘Heavily degraded mangroves’ and ‘Nipa monoculture’. suggested citiation: Stephani, A.; Oswald, P.; Koellner, T.; Wegmann, M., 2016. Tanintharyi Region Mangrove Forest dataset (2014). Point of contact: annastephani@gmx.de and patrickoswald@fauna-flora.org

This is a dataset of possible mining areas in Myanmar that was digitized from a systematic search of high-resolution imagery available on Google Earth and Bing Aerial. Digitizing took place in late 2015 and early 2016 with the most recent imagery available (usually 2014 or 2015). A field in the attribute table called "Certainty" indicates the how confident we were that the digitized polygon is a mine, rather than some other type of ground disturbance.

Geotiff download link: http://bit.ly/2IjLZtw This dataset is the result of a land cover analysis for Myanmar's Tanintharyi Region based on March, 2016 Landsat 8 OLI imagery. The primary purpose of the study was to map natural forest for each of four ecological forest types (Mangrove, Mixed Deciduous, Lowland Evergreen, Upland Evergreen). A number of other land use/land cover types are also included in the dataset, including human settlement areas, rice paddyfields, and agroforestry plantations. This dataset is the original version generated according to the methodology outlined in the corresponding manuscript. [Citation: Connette, G., P. Oswald, M. Songer, and P. Leimgruber. 2016. Mapping distinct forest types improves overall forest identification based on multi-spectral Landsat imagery. Remote Sensing 8: 882.] [Spatial reference: WGS84 UTM47N] Original dataset title: Tanintharyi Region Land Cover - March 2016 (Original)

Geotiff download link: http://bit.ly/2ToMIjL This dataset is the result of a land cover analysis for Myanmar's Tanintharyi Region based on March, 2016 Landsat 8 OLI imagery. The primary purpose of the study was to map natural forest for each of four ecological forest types (Mangrove, Mixed Deciduous, Lowland Evergreen, Upland Evergreen). A number of other land use/land cover types are also included in the dataset, including human settlement areas, rice paddyfields, and agroforestry plantations. This dataset is a REVISED version of the land cover map generated according to the methodology outlined in the corresponding manuscript (see below for citation). This version has been manually edited to fill cloud holes using 2015 data and to fix a number of obvious mis-classifications, particularly for oil palm and settlement areas. [Citation: Connette, G., P. Oswald, M. Songer, and P. Leimgruber. 2016. Mapping distinct forest types improves overall forest identification based on multi-spectral Landsat imagery. Remote Sensing 8: 882.] [Spatial reference: WGS84 UTM47N] Original dataset title: Tanintharyi Region Land Cover - March 2016 (Improved)

Location of Sea Ports in Myanmar. Attributes include Port Name, Port Type (Sea port and deep Sea Port Projects), as well as Source. Data source: Myanma Port Authority, http://www.mpa.gov.mm/ January 2019

Low-lying Areas below 5 meters elevation. This dataset is derived from the Multi-Error-Removed Improved-Terrain / MERIT DEM.

Hard To Reach Village Tracts in Myanmar. The General Administration Department defines the Hard to Reach areas in three categories (Moderate, Hard, Very Hard) according to road accessibility across seasons. Order No. 44/219, 8 May 2019 by the Ministry of Planning and Finance Original dataset title: Hard to Reach Village Tract May 2019

Hard To Reach Towns in Myanmar. The General Administration Department defines the Hard to Reach areas in three categories (Moderate, Hard, Very Hard) according to road accessibility across seasons Order No. 44/219, 8 May 2019 by the Ministry of Planning and Finance Original dataset title: Hard To Reach Towns May 2019

Formal Sector Schools for Basic Education in Myanmar. Location data was verified in May 2019 by the Ministry of Education (MoE) and compiled by MIMU. Some school locations have yet to be confirmed and some may not be included in this dataset. Original dataset title: Formal Sector School Location Lower Myanmar (2019)

Formal Sector Schools for Basic Education in Myanmar. Location data was verified in October 2019 by the Ministry of Education (MoE) and compiled by MIMU. Some school locations have yet to be confirmed and some may not be included in this dataset. Original dataset title: Formal Sector School Location Upper Myanmar (2019)

Outputs from seasonal river morphological monitoring system showing erosion and deposition areas along the Ayeyarwady River, Myanmar after the end of every monsoon season developed under the USAID funded SERVIR-Mekong programme. This dataset uses on Landsat 5-7 satellite data series covering 31-year time scale from 1988 to 2019. You can find tiff files from 1988 to 2019 here : http://geonode.themimu.info/static/riverbankerosin.html https://myit-servir.adpc.net/ Original dataset title: Ayeyarwady river bank erosion 1988-2019

Pyithu Hluttaw constituencies follow the administrative boundaries of townships (total 330). Note that this file shows only elected representation per constituency, but not the number of military appointees in the hluttaw. It is important to note that constituencies boundaries for state/region Hluttaw constituencies are only indicative Original dataset title: Pyithu Hluttaw (Lower House) Constituencies 2020

The boundaries of Amyotha Hluttaw constituencies are defined by the Union Election Commission. The 2020 Constituencies establishes 12 such constituencies per State/Region (total 168). They are determined on the basis of population statistics. . Original dataset title: Amyotha Hluttaw (Upper House) Constituencies 2020

Location of Airports in Myanmar. Attributes include operational and non-operational fields. Data source: Mission Aviation Fellowship International (MAF International - Myanmar), https://www.mafint.org/Myanmar.html September 2020

Road network with main, secondary and tertiary roads. This network-based connectivity approach mainly uses population centres, such as, Union capital, State/Region capital, Main town, other town and village location, as the bases to road network. Main Road links ST/R Capital to Main Towns and to Neighbouring countries. Secondary Road links a Major Road to another Main Road. Tertiary Road links main town to other-town and other-town to other-town. Original dataset title: Myanmar Road Network 2022

Lakes and Dams in Myanmar. This dataset was extracted from Landsat satellite images, Sentinel-1 ESA satellite images, and Google satellite images. The United Nations and the MIMU specifically do not make any warranties or representations as to the accuracy or completeness of this dataset nor does it imply official endorsement or acceptance by the United Nations.

Myanmar national rail network (lines). Updated railway data in March 2022 based on Google Satellite images and the Ministry of Transport and Communications. This layer shows the transportation network based on available information from Google Earth. Parts of the network may not be functioning for various reasons such as landslides. The status of the railway has not been verified. Place names from General Administration Department (GAD), are available in Myanmar Unicode. Transliteration in roman script by MIMU. Lines by MIMU using Google Satellite images as a reference. Original dataset title: Myanmar Railway Network 2022

Towns are urban areas divided into wards. This version is based on the latest MIMU Pcode version 9.4 and information provided by field offices and government departments. Compared to the previous version, it includes one new town named Law Pi Ta was formed in Loikaw Township in Kayah State. The new town name is drawn from the previous village tract name. Place names from General Administration Department (GAD), are available in Myanmar Unicode. Transliteration in roman script by MIMU. Place names are in line with the general cartographic practice to reflect the names of such places as designated by the host governments.

Polygons of Village Tract boundaries (Admin4) for the Yangon Region, based on the latest MIMU Pcode version 9.4 . Place names from General Administration Department (GAD), are available in Myanmar Unicode. Transliteration in roman script by MIMU. Place names are in line with the general cartographic practice to reflect the names of such places as designated by the host governments. The United Nations encourages authorities to review place names which include terms that may be viewed or perceived as derogatory.