Automatic building detection from high-resolution satellite imaging images has many applications. Understanding socioeconomic development and keeping track of population migrations are essential for effective civic planning. These civil feature systems may also help update maps after natural disasters or in geographic regions undergoing dramatic population expansion. To accomplish the desired goal, a variety of image processing techniques were employed. They are often inaccurate or take a long time to process. Convolutional neural networks (CNNs) are being designed to extract buildings from satellite images, based on the U-Net, which was first developed to segment medical images. The minimal number of images from the open dataset, in RGB format with variable shapes, reveals one of the advantages of the U-Net; that is, it develops excellent accuracy from a limited amount of training material with minimal effort and training time. The encoder portion of U-Net was altered to test the feasibility of using a transfer learning facility. VGGNet and ResNet were both used for the same purpose. The findings of these models were also compared to our own bespoke U-Net, which was designed from the ground up. With an accuracy of 84.9%, the VGGNet backbone was shown to be the best feature extractor. Compared to the current best models for tackling a similar problem with a larger dataset, the present results are considered superior.
This article discusses the design, development, and usability assessment of a mobile system for producing hydrological predictions and sending flood warnings in response to the desire for human-centered technology to better the management of flood occurrences. Our work acts as a bibliographic reference for understanding what others have attempted and found, as well as gives an integrated set of recommendations. Furthermore, our guidelines offer guidance to aid in the design of mobile GIS-based hydrological models for mobile devices. We concentrate on the full design of a human–computer interaction framework for an effective flood prediction and warning system. In addition, we analyze and address the current user needs and requirements for building a user interface for mobile real-time flood forecasting in a methodical manner. Although a functional prototype was created, the primary objective of this research was to comprehend the complexity of possible users’ demands and actual use situations in order to solve the problem of comparable systems being difficult to use. After consulting with possible consumers, application design standards were established and implemented in the initial prototype. Focusing on user demands and attitudes, special consideration was given to the usability of the mobile interface. To develop the application, a variety of assessment methods are added. The conclusion of the examination was that the system is efficient and effective.
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