Land Feature Identification and Prediction of Burewala Using Machine Learning

Abstract

Burewala's study area is made up of many geographical types with various attributes. There are various types of land based on features, such as agricultural land, urban land, river channel lands, roads, and suburban regions. between 30 and 43 years. The observed terrain features of Burewala differ from those of the native land. For instance, the development of towns and communities has event in the transformation of rural land into urban land (problem). Artificial neural networks, deep learning, regression analysis, and mathematical analysis are some of the machine learning approaches that we would be using. I use methods using the satellite photos to map the Burewala land based on features. I have created a map that displays the land characteristics of agricultural areas. Using features from 1980, I created a map that shows four to five distinct types of land. To lower the in accuracy, I would collect additional satellite pictures from 1990 of the same research region, with the same seasons and parameters applied. After ten years, I would remap all those chosen characteristics for the 1990. I would follow the same procedures and make new maps for the 2000 photos after ten more years of 2000.I would do the 2010 again. Lastly, I would make advantage of 2023. Options I would watch for changes between 1980 and 2023, both in terms of decreases and increases. Generally speaking, based on my observations—I reside in Burewala. I am aware of numerous instances when agricultural land has been converted into well-stalled urban areas. The impact that my work will ultimately have is on the decline of crop fields and the increase in urbanisation. It would expand the field of study and open up new avenues. Understanding the urban development factors and which businesses are suitable would be beneficial. My outcomes would be heavily reliant on machine learning techniques. I learned the results using machine learning. I would assess the numerical values of the areas that have changed over the course of 43 years. The outcomes of the machine learning test and judge. I went into the field, took a photo, and provided solid proof that the algorithm matched the data. If they did not match, I would use different machine learning approaches to determine the risk factors analysis.