Although the terms “land usage” and “land cover” are frequently used interchangeably, each term has a distinct definition. The term “land cover” describes the material that covers the surface of the ground, such as vegetation, urban infrastructure, water, bare soil, etc. Land cover identification creates the baseline data for tasks like thematic mapping and change detection analyses. The term “land use” describes the function that a piece of land performs, such as agriculture, wildlife habitat, or recreation.

Agronomists and agricultural organizations responsible for land management employ accurate land cover information in a variety of applications, including hydrological modeling, environmental management plans, effective infrastructure planning, and agronomists. In today’s rapidly urbanized world, where “55% of the world’s population lives in urban areas, a proportion that is expected to increase to 68% by 2050″ (World Urbanization Prospects, 2018), accurate and up-to-date land cover information becomes vital.

Latest updated land-use information can provide support to decision makers when responding to issues and challenges hampering effective urban governance.

Conventional means of land cover digitization using manual/ semi-automated approach is time, resource intensive and non-scalable approach. We used remote sensing and advanced machine learning techniques to automate land cover classification using satellite imageries.

The study shows our potential of utilizing remote sensing and boosted decision tree models for automatic land cover classification using satellite images. The classified land cover is categorized into 7 classes, namely, Built-up, Tree, Grass, Crop, Water, Shrub and Other.

To focus on results, we selected Ulaanbaatar as the area of interest. Ulaanbaatar is the capital and largest city of Mongolia. With a population of about 1.5 million as of 2020, it contains almost half of Mongolia’s total population. This city is the cultural, industrial and financial heart, the center of Mongolia’s transport network and connected by rail to both the Trans-Siberian Railway in Russia and the Chinese railway system.

Benefits of Using EO Factory Technology

Based on SAAS Data Strategy and using inbuilt preprocessing toolkits enhancing the power of EO Factory Platform, we are able to produce results in very short amounts of time making EOfactory the leading Platform in the imagery Data Processing Field.

In most of the use cases, multiple softwares are needed to achieve the goals of many users, but with EO Factory being the one-stop destination for all the problems, users are able to produce all their results in one platform with highly satisfying results.