Search results
Results from the WOW.Com Content Network
The CMDA administers the Chennai Metropolitan Region, spread over an area of 5,904 km 2 (2,280 sq mi) and covers the districts of Chennai, Thiruvallur, Chengalpattu, Ranipet and Kancheepuram. [1] It was set up for the purposes of planning, co-ordination, supervising, promoting and securing the planned development of the Chennai Metropolitan Area .
The organisation was known as Calcutta Metropolitan Development Authority (CMDA) and retains its previous logo. KMDA is functioning under the administrative control of Department of Urban Development and Municipal Affairs of Government of West Bengal .
CMDA can refer to: Christian Medical and Dental Associations , a professional medical association Chennai Metropolitan Development Authority , is the nodal planning agency within the Chennai Metropolitan Area
Land use capability maps are maps created to represent the potential uses of a "unit" of land. They are measured using various indicators, although the most common are five physical factors ( rock type , soil type , slope, erosion degree and type, and vegetation).
Cumulative CO2 emissions from land-use change (as of 2021). Emissions from land-use change can be positive or negative depending on whether these changes emit (positive, brown on the map) or sequester (negative) carbon (green on the map). Land use is an umbrella term to describe what happens on a parcel of land.
Windows 95, 98, ME have a 4 GB limit for all file sizes. Windows XP has a 16 TB limit for all file sizes. Windows 7 has a 16 TB limit for all file sizes. Windows 8, 10, and Server 2012 have a 256 TB limit for all file sizes. Linux. 32-bit kernel 2.4.x systems have a 2 TB limit for all file systems.
Main page; Contents; Current events; Random article; About Wikipedia; Contact us; Help; Learn to edit; Community portal; Recent changes; Upload file
A supervised classification is a system of classification in which the user builds a series of randomly generated training datasets or spectral signatures representing different land-use and land-cover (LULC) classes and applies these datasets in machine learning models to predict and spatially classify LULC patterns and evaluate classification accuracies.