Building Detection in Earthquake Prone Urban Area with Satellite Imagery Data Using Deep Learning

2021 ◽  
Author(s):  
Edy Irwansyah ◽  
Alexander A. Santoso. Gunawan ◽  
Calvin Surya ◽  
Dewa Ayu Defina Audrey Nathania
Keyword(s):  
Deep Learning ◽  
Urban Area ◽  
Satellite Imagery ◽  
Building Detection ◽  
Imagery Data

scholarly journals DEEP LEARNING APPROACH FOR URBAN MAPPING

2022 ◽  
Vol XLVI-4/W3-2021 ◽  
pp. 261-266
Author(s):  
C. Najjaj ◽  
H. Rhinane ◽  
A. Hilali
Keyword(s):  
Deep Learning ◽  
Satellite Imagery ◽  
Satellite Images ◽  
Semantic Segmentation ◽  
Learning Approach ◽  
Building Detection ◽  
Building Extraction ◽  
Great Precision ◽  
Proposed Model ◽  
Weight Model

Abstract. Researchers in computer vision and machine learning are becoming increasingly interested in image semantic segmentation. Many methods based on convolutional neural networks (CNNs) have been proposed and have made considerable progress in the building extraction mission. This other methods can result in suboptimal segmentation outcomes. Recently, to extract buildings with a great precision, we propose a model which can recognize all the buildings and present them in mask with white and the other classes in black. This developed network, which is based on U-Net, will boost the model's sensitivity. This paper provides a deep learning approach for building detection on satellite imagery applied in Casablanca city, Firstly, to begin we describe the terminology of this field. Next, the main datasets exposed in this project which’s 1000 satellite imagery. Then, we train the model UNET for 25 epochs on the training and validation datasets and testing the pretrained weight model with some unseen satellite images. Finally, the experimental results show that the proposed model offers good performance obtained as a binary mask that extract all the buildings in the region of Casablanca with a higher accuracy and entirety to achieve an average F1 score on test data of 0.91.

scholarly journals Semantic segmentation of PolSAR image data using advanced deep learning model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rajat Garg ◽  
Anil Kumar ◽  
Nikunj Bansal ◽  
Manish Prateek ◽  
Shashi Kumar
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Urban Area ◽  
Urban Areas ◽  
Learning Algorithms ◽  
Semantic Segmentation ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Deep Learning Model

AbstractUrban area mapping is an important application of remote sensing which aims at both estimation and change in land cover under the urban area. A major challenge being faced while analyzing Synthetic Aperture Radar (SAR) based remote sensing data is that there is a lot of similarity between highly vegetated urban areas and oriented urban targets with that of actual vegetation. This similarity between some urban areas and vegetation leads to misclassification of the urban area into forest cover. The present work is a precursor study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission and aims at minimizing the misclassification of such highly vegetated and oriented urban targets into vegetation class with the help of deep learning. In this study, three machine learning algorithms Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) have been implemented along with a deep learning model DeepLabv3+ for semantic segmentation of Polarimetric SAR (PolSAR) data. It is a general perception that a large dataset is required for the successful implementation of any deep learning model but in the field of SAR based remote sensing, a major issue is the unavailability of a large benchmark labeled dataset for the implementation of deep learning algorithms from scratch. In current work, it has been shown that a pre-trained deep learning model DeepLabv3+ outperforms the machine learning algorithms for land use and land cover (LULC) classification task even with a small dataset using transfer learning. The highest pixel accuracy of 87.78% and overall pixel accuracy of 85.65% have been achieved with DeepLabv3+ and Random Forest performs best among the machine learning algorithms with overall pixel accuracy of 77.91% while SVM and KNN trail with an overall accuracy of 77.01% and 76.47% respectively. The highest precision of 0.9228 is recorded for the urban class for semantic segmentation task with DeepLabv3+ while machine learning algorithms SVM and RF gave comparable results with a precision of 0.8977 and 0.8958 respectively.

scholarly journals The Utilization of Satellite Imagery Data to Predict Hydrology Characteristics in Dodokan Watershed

2016 ◽  
Vol 33 ◽  
pp. 36-43 ◽  
Author(s):  
Sri Malahayati Yusuf ◽  
Kukuh Murtilaksono ◽  
Mahendra Harjianto ◽  
Endah Herlina
Keyword(s):  
Satellite Imagery ◽  
Imagery Data

scholarly journals A Deep Learning Approach on Building Detection from Unmanned Aerial Vehicle-Based Images in Riverbank Monitoring

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3921 ◽  
Author(s):  
Wuttichai Boonpook ◽  
Yumin Tan ◽  
Yinghua Ye ◽  
Peerapong Torteeka ◽  
Kritanai Torsri ◽  
...  
Keyword(s):  
Deep Learning ◽  
Network Architecture ◽  
Semantic Segmentation ◽  
Water Levels ◽  
Building Detection ◽  
Network Training ◽  
Building Information ◽  
Open Standard ◽  
Uav Images ◽  
Uav Image

Buildings along riverbanks are likely to be affected by rising water levels, therefore the acquisition of accurate building information has great importance not only for riverbank environmental protection but also for dealing with emergency cases like flooding. UAV-based photographs are flexible and cloud-free compared to satellite images and can provide very high-resolution images up to centimeter level, while there exist great challenges in quickly and accurately detecting and extracting building from UAV images because there are usually too many details and distortions on UAV images. In this paper, a deep learning (DL)-based approach is proposed for more accurately extracting building information, in which the network architecture, SegNet, is used in the semantic segmentation after the network training on a completely labeled UAV image dataset covering multi-dimension urban settlement appearances along a riverbank area in Chongqing. The experiment results show that an excellent performance has been obtained in the detection of buildings from untrained locations with an average overall accuracy more than 90%. To verify the generality and advantage of the proposed method, the procedure is further evaluated by training and testing with another two open standard datasets which have a variety of building patterns and styles, and the final overall accuracies of building extraction are more than 93% and 95%, respectively.

scholarly journals Drought risk in the Bolivian Altiplano associated with El Niño Southern Oscillation using satellite imagery data

2019 ◽  
Author(s):  
Claudia Canedo-Rosso ◽  
Stefan Hochrainer-Stigler ◽  
Georg Pflug ◽  
Bruno Condori ◽  
Ronny Berndtsson
Keyword(s):  
Satellite Imagery ◽  
El Niño ◽  
Crop Production ◽  
Vegetation Index ◽  
Crop Yields ◽  
El Nino ◽  
Southern Oscillation ◽  
Drought Risk ◽  
Bolivian Altiplano ◽  
Imagery Data

Abstract. Drought is a major natural hazard in the Bolivian Altiplano that causes large losses to farmers, especially during positive ENSO phases. However, empirical data for drought risk estimation purposes are scarce and spatially uneven distributed. Due to these limitations, similar to many other regions in the world, we tested the performance of satellite imagery data for providing precipitation and temperature data. The results show that droughts can be better predicted using a combination of satellite imagery and ground-based available data. Consequently, the satellite climate data were associated with the Normalized Difference Vegetation Index (NDVI) in order to evaluate the crop production variability. Moreover, NDVI was used to target specific drought hotspot regions. Furthermore, during positive ENSO phase (El Niño years), a significant decrease in crop yields can be expected and we indicate areas where losses will be most pronounced. The results can be used for emergency response operations and enable a pro-active approach to disaster risk management against droughts. This includes economic-related and risk reduction strategies such as insurance and irrigation.

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