MODEL FOR DETECTION AND CLASSIFICATION OF LANDSLIDES USING SATELLITE DATA

Abstract

This invention provides a landslide detection and classification model using satellite data, combining convolutional neural networks with attention mechanisms. Designed to distinguish landslide-prone areas accurately, the model offers a high-accuracy, automated solution for predictive landslide monitoring and disaster response. The model’s robust classification capabilities improve disaster preparedness, reducing risks to life and infrastructure.

Specification

Description:FIELD OF THE INVENTION
This invention pertains to artificial intelligence and remote sensing technology, specifically focusing on a model designed for the automatic detection and classification of landslides. Utilizing satellite data, convolutional neural networks (CNN), and attention mechanisms, the invention provides a rapid and accurate solution for landslide prediction, beneficial for disaster prevention and construction planning.
BACKGROUND OF THE INVENTION
Landslides pose significant risks to human life, infrastructure, and the environment, especially in developing countries like India, where infrastructure development in hilly areas increases susceptibility to landslides. Traditional methods for landslide detection involve field surveys and manual inspections, which are time-consuming and labor-intensive. These conventional techniques often fail to provide timely and accurate early warnings, limiting their effectiveness in mitigating disaster risks.
This invention addresses these limitations by proposing an automatic landslide detection and classification model using satellite data. By leveraging deep learning, particularly convolutional neural networks (CNN) and attention modules, the model efficiently classifies landslide-prone areas from satellite images. The invention significantly enhances the speed, accuracy, and reliability of landslide detection, thereby reducing the need for extensive human resources and time, providing early warnings, and minimizing the potential for disaster-related loss of life and property.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
This invention provides a model for detecting and classifying landslides using satellite data. The model uses convolutional neural networks with attention modules to improve image classification accuracy, thereby distinguishing landslide from non-landslide areas with high precision. The system is trained on labeled datasets and utilizes data augmentation to increase its robustness and adaptability. Through real-time testing on new satellite images, the model offers a reliable, scalable solution for landslide prediction and monitoring.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms "a"," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes" and/or "including," when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", "third", and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The proposed model for landslide detection and classification operates by analyzing satellite imagery to determine areas at risk for landslides. Initially, satellite images are acquired from reliable sources, including RADARSAT-2, Envisat ASAR, and ALOS PALSAR. These images provide high-resolution data that captures essential geographic and morphological details necessary for landslide prediction.
The collected images undergo a pre-processing phase, where image quality is enhanced through resizing and augmentation techniques. Image resizing ensures uniformity across the dataset, facilitating efficient analysis. Augmentation techniques are applied to increase dataset diversity, improving the model's ability to generalize across various environments and reducing overfitting. The dataset is divided into two classes: landslide and non-landslide, allowing the model to learn distinguishing features during training.
The model is structured around convolutional neural networks (CNN), which are effective for image classification tasks due to their hierarchical feature extraction capabilities. Attention mechanisms are incorporated to focus the model's learning on minor, yet significant, features within landslide images, enhancing classification accuracy. Various CNN architectures, such as ResNet-50, ResNet-101, and GoogleNet, are employed as backbone networks to provide robustness and precision in feature extraction.
Training is conducted on labeled datasets, with the model optimized to minimize loss functions and improve classification performance. To further enhance model accuracy, hyperparameters are fine-tuned, and transfer learning techniques are applied, adapting pre-trained models to the landslide classification context. The training dataset is iteratively processed to achieve optimal weights, while the model's performance is evaluated using a test dataset.
Once trained, the model processes real-time satellite images, classifying regions based on landslide risk. Predictions are categorized into landslide and non-landslide areas, providing crucial data for early warning systems. The model's accuracy is validated against real-time images, and if necessary, the model undergoes further adjustments in architecture and hyperparameters to achieve reliable results.
The automatic detection and classification model not only streamlines landslide identification but also enhances disaster preparedness by offering scalable and repeatable predictions across diverse landscapes. This innovation reduces the dependency on manual monitoring, conserves human resources, and provides actionable insights that mitigate risks associated with landslides.
, Claims:1. A model for detecting and classifying landslides using satellite data, comprising data collection, pre-processing, CNN-based image classification, and an attention module to enhance detection accuracy.
2. The model as claimed in Claim 1, wherein satellite images are resized and augmented during pre-processing to improve data quality and enhance model robustness.
3. The model as claimed in Claim 1, wherein CNN architectures, including ResNet-50, ResNet-101, and GoogleNet, are utilized as backbone networks to optimize feature extraction in landslide classification.
4. The model as claimed in Claim 1, wherein an attention module is integrated with the CNN to focus on minor features, improving the accuracy of landslide detection.
5. The model as claimed in Claim 1, wherein the training and testing framework divides datasets into landslide and non-landslide categories, allowing performance evaluation and optimization.
6. The model as claimed in Claim 1, wherein it employs data augmentation to enhance model generalization and adaptability across varied environments.
7. A method for landslide detection as claimed in Claim 1, involving real-time classification of satellite images into landslide and non-landslide categories for predictive monitoring.
8. The model as claimed in Claim 1, wherein it incorporates transfer learning to optimize CNN performance and improve prediction accuracy on new datasets.
9. The model as claimed in Claim 1, wherein it provides a scalable, automated solution for landslide classification, suitable for disaster management and infrastructure planning.

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