Design and Implementation of Land Area Calculation for Maps Using Mask Region Based Convolutional Neural Networks Deep Neural Network

Maps of the land are developed by the surveyor, map developer according to survey of land. In such maps land boundaries are shown using property lines. So the area of land is also mentioned in the maps to the valuation of the property. Area calculation is one of the main work of the surveyor so it is important for him to calculate it fast. So we have implemented a system which can help surveyor, land map developers to calculate the area. We implemented this using image processing and the deep learning model mask region based convolutional neural network (RCNN). For better results, we implemented this at a basic level. At base level synthetic dataset consists of 2 dimensional images of different geometry shapes (triangle, quadrilateral, pentagon, hexagon, octagon) and training our model to detect the shape in the image and based on this further process of area calculation of that shape takes place. This solution is unique for land developers because it uses deep learning and image processing to obtain results.