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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 .
The Chennai Metropolitan Development Authority (CMDA) is the nodal agency that handles town planning and development within the metro area. In 1974, an area encompassing 1,189 km 2 (459 sq mi) around the city was designated as the metropolitan area which was subsequently expanded to 5,904 km 2 (2,280 sq mi) in 2022.
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).
The cost of land use planning is usually high, generally because of poor investment and the lack of anticipation of technology. Land use planning theory has largely been shaped by case studies of cities in the Global North. Countries all over the world, particularly in the Global South, are seeing population booms and rapid urbanization.
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.
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
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.