Journal ArticleDOI
Recognizing Global Reservoirs From Landsat 8 Images: A Deep Learning Approach
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TLDR
In this paper, a convolutional neural network (CNN)-based framework was proposed to recognize global reservoirs from Landsat 8 imageries, which achieved state-of-the-art accuracy of 91.45%.Abstract:
Man-made reservoirs are key components of terrestrial hydrological systems. Identifying the location and number of reservoirs is the premise for studying the impact of human activities on water resources and environmental changes. While complete bottom-up censuses can provide a comprehensive view of the reservoir landscape, they are time-consuming and laborious and are thus infeasible on a global scale. Moreover, it is challenging to distinguish man-made reservoirs from natural lakes in remote sensing images. This study proposes a convolutional neural network (CNN)-based framework to recognize global reservoirs from Landsat 8 imageries. On the basis of the HydroLAKES dataset, a Landsat 8 cloud-free mosaic of 2017 was clipped for each feature (reservoir or lake) and was resized into 224 × 224 patches, which were collected as training and testing samples. Compared to other deep learning methods (Alexnet and VGG) and state-of-the-art traditional machine learning methods (support vector machine, random forest, gradient boosting, and bag-of-visual-words), we found that fine-tuning the pretrained CNN model, ResNet-50, could reach the highest accuracy (91.45%). Application cases in Kansas (USA, North America), Mpumalanga (South Africa, Africa), and Kostanay (Kazakhstan, Asia) resulted in classification accuracies of better than 99%, which showed the applicability of the proposed ResNet-50 model to the extraction of reservoirs from a vast amount of moderate resolution images. The framework that was developed in this paper is the first attempt to combine remote sensing big data and the deep learning technique to the recognition of reservoirs at a global scale.read more
Citations
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Journal ArticleDOI
An Urban Water Extraction Method Combining Deep Learning and Google Earth Engine
TL;DR: This article proposed a new method by combining Google Earth Engine (GEE) with a multiscale convolutional neural network (MSCNN) to extract urban water from Landsat images, which can be summarized as “offline training and online prediction” (OTOP).
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A Deep Learning Method of Water Body Extraction From High Resolution Remote Sensing Images With Multisensors
TL;DR: Wang et al. as mentioned in this paper proposed a new network called the dense-local-feature-compression (DLFC) network aiming at extracting water body from different remote sensing images automatic.
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Evaluation of Robust Spatial Pyramid Pooling Based on Convolutional Neural Network for Traffic Sign Recognition System
TL;DR: This paper investigates the state-of-the-art of various object detection systems (Yolo V3, Resnet 50, Densenet, and Tiny YoloV3) combined with spatial pyramid pooling (SPP), and shows that Yolo V 3 SPP strikes the best total BFLOPS, mAP, and mAP measures, and SPP can improve the performance of all models in the experiment.
Journal ArticleDOI
Synthetic Data generation using DCGAN for improved traffic sign recognition
TL;DR: This paper analyzes and discusses CNN models incorporating different backbone architectures and feature extractors, focusing on Resnet 50 and Densenet for object detection and shows that combining original images and synthetic images in the dataset for training can improve intersection over union (IoU) and traffic sign recognition performance.
Journal ArticleDOI
GeoDAR: georeferenced global dams and reservoirs dataset for bridging attributes and geolocations
Jida Wang,Blake A Walter,Fangfang Yao,Chunqiao Song,Meng Ding,Abu Sayeed Maroof,Jingying Zhu,Chenyu Fan,Jordan M. McAlister,Safat Sikder,Yongwei Sheng,George H. Allen,Jean-François Crétaux,Yoshihide Wada +13 more
TL;DR: GeoDAR as discussed by the authors is a georeferenced global Dams And Reservoirs dataset, created by utilizing the Google Maps geocoding application programming interface (API) and multi-source inventories.
References
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