DEEP LEARNING APPROACH FOR URBAN MAPPING

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.

Download Full-text

Semantic Segmentation-Based Building Footprint Extraction Using Very High-Resolution Satellite Images and Multi-Source GIS Data

Remote Sensing ◽

10.3390/rs11040403 ◽

2019 ◽

Vol 11 (4) ◽

pp. 403 ◽

Cited By ~ 21

Author(s):

Weijia Li ◽

Conghui He ◽

Jiarui Fang ◽

Juepeng Zheng ◽

Haohuan Fu ◽

...

Keyword(s):

High Resolution ◽

Satellite Images ◽

Data Augmentation ◽

Semantic Segmentation ◽

Building Detection ◽

Building Extraction ◽

Three Solutions ◽

High Resolution Satellite Images ◽

Building Footprint ◽

Map Data

Automatic extraction of building footprints from high-resolution satellite imagery has become an important and challenging research issue receiving greater attention. Many recent studies have explored different deep learning-based semantic segmentation methods for improving the accuracy of building extraction. Although they record substantial land cover and land use information (e.g., buildings, roads, water, etc.), public geographic information system (GIS) map datasets have rarely been utilized to improve building extraction results in existing studies. In this research, we propose a U-Net-based semantic segmentation method for the extraction of building footprints from high-resolution multispectral satellite images using the SpaceNet building dataset provided in the DeepGlobe Satellite Challenge of IEEE Conference on Computer Vision and Pattern Recognition 2018 (CVPR 2018). We explore the potential of multiple public GIS map datasets (OpenStreetMap, Google Maps, and MapWorld) through integration with the WorldView-3 satellite datasets in four cities (Las Vegas, Paris, Shanghai, and Khartoum). Several strategies are designed and combined with the U-Net–based semantic segmentation model, including data augmentation, post-processing, and integration of the GIS map data and satellite images. The proposed method achieves a total F1-score of 0.704, which is an improvement of 1.1% to 12.5% compared with the top three solutions in the SpaceNet Building Detection Competition and 3.0% to 9.2% compared with the standard U-Net–based method. Moreover, the effect of each proposed strategy and the possible reasons for the building footprint extraction results are analyzed substantially considering the actual situation of the four cities.

Download Full-text

LUNG NODULE SEMANTIC SEGMENTATION WITH BI-DIRECTION FEATURES USING U-INET

Journal of medical pharmaceutical and allied sciences ◽

10.22270/jmpas.v10i5.1454 ◽

2021 ◽

Vol 10 (5) ◽

pp. 3494-3499

Author(s):

Eali Stephen Neal Joshua

Keyword(s):

Deep Learning ◽

Ct Scan ◽

Cancer Diagnosis ◽

Lung Nodule ◽

Visual Quality ◽

Semantic Segmentation ◽

Surrounding Tissue ◽

Learning Approach ◽

Proposed Model ◽

Severity Of The Disease

It's difficult to detect lung cancer and determine the severity of the disease without a CT scan of the lungs. The anonymity of nodules, as well as physical characteristics such as curvature and surrounding tissue, suggest that CT lung nodule segmentation has limitations. According to the study, a new, resource-efficient deep learning architecture dubbed U-INET is required. When a doctor orders a computed tomography (CT) scan for cancer diagnosis, precise and efficient lung nodule segmentation is required. Due to the nodules' hidden form, poor visual quality, and context, lung nodule segmentation is a challenging job. The U-INET model architecture is given in this article as a resource-efficient deep learning approach for dealing with the problem. To improve segmentation operations, it also includes the Mish non-linearity functions and mask class weights. Furthermore, the LUNA-16 dataset, which included 1200 lung nodules, was heavily utilized to train and evaluate the proposed model. The U-INET architecture outperforms the current U-INET model by 81.89 times and reaches human expert level accuracy.

Download Full-text

Self-Attention in Reconstruction Bias U-Net for Semantic Segmentation of Building Rooftops in Optical Remote Sensing Images

Remote Sensing ◽

10.3390/rs13132524 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2524

Author(s):

Ziyi Chen ◽

Dilong Li ◽

Wentao Fan ◽

Haiyan Guan ◽

Cheng Wang ◽

...

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Semantic Segmentation ◽

Extraction Methods ◽

The Self ◽

Optical Remote Sensing ◽

Building Extraction ◽

Learning Models ◽

Remote Sensing Images ◽

Segmentation Methods

Deep learning models have brought great breakthroughs in building extraction from high-resolution optical remote-sensing images. Among recent research, the self-attention module has called up a storm in many fields, including building extraction. However, most current deep learning models loading with the self-attention module still lose sight of the reconstruction bias’s effectiveness. Through tipping the balance between the abilities of encoding and decoding, i.e., making the decoding network be much more complex than the encoding network, the semantic segmentation ability will be reinforced. To remedy the research weakness in combing self-attention and reconstruction-bias modules for building extraction, this paper presents a U-Net architecture that combines self-attention and reconstruction-bias modules. In the encoding part, a self-attention module is added to learn the attention weights of the inputs. Through the self-attention module, the network will pay more attention to positions where there may be salient regions. In the decoding part, multiple large convolutional up-sampling operations are used for increasing the reconstruction ability. We test our model on two open available datasets: the WHU and Massachusetts Building datasets. We achieve IoU scores of 89.39% and 73.49% for the WHU and Massachusetts Building datasets, respectively. Compared with several recently famous semantic segmentation methods and representative building extraction methods, our method’s results are satisfactory.

Download Full-text

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

10.1109/aidas53897.2021.9574257 ◽

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

Download Full-text

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

Sensors ◽

10.3390/s18113921 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3921 ◽

Cited By ~ 17

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.

Download Full-text

Oil Spill Discrimination of SAR Satellite Images Using Deep Learning Based Semantic Segmentation

Communications in Computer and Information Science - Computing Science, Communication and Security ◽

10.1007/978-3-030-76776-1_9 ◽

2021 ◽

pp. 127-139

Author(s):

V. Sudha ◽

Anna Saro Vijendran

Keyword(s):

Deep Learning ◽

Oil Spill ◽

Satellite Images ◽

Semantic Segmentation

Download Full-text

Segmentation of Satellite Images Using Contractive Autoencoder (CAE) Aided Deep Learning Approach

Lecture Notes in Networks and Systems - Advances in Intelligent Computing and Communication ◽

10.1007/978-981-16-0695-3_62 ◽

2021 ◽

pp. 667-675

Author(s):

Manami Barthakur ◽

Kandarpa Kumar Sarma

Keyword(s):

Deep Learning ◽

Satellite Images ◽

Learning Approach

Download Full-text

U-Net-Id, an Instance Segmentation Model for Building Extraction from Satellite Images—Case Study in the Joanópolis City, Brazil

Remote Sensing ◽

10.3390/rs12101544 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1544 ◽

Cited By ~ 5

Author(s):

Fabien H. Wagner ◽

Ricardo Dalagnol ◽

Yuliya Tarabalka ◽

Tassiana Y. F. Segantine ◽

Rogério Thomé ◽

...

Keyword(s):

Network Architecture ◽

Satellite Images ◽

Semantic Segmentation ◽

Developed Countries ◽

Building Extraction ◽

Neural Network Architecture ◽

Rgb Images ◽

The City ◽

Instance Segmentation

Currently, there exists a growing demand for individual building mapping in regions of rapid urban growth in less-developed countries. Most existing methods can segment buildings but cannot discriminate adjacent buildings. Here, we present a new convolutional neural network architecture (CNN) called U-net-id that performs building instance segmentation. The proposed network is trained with WorldView-3 satellite RGB images (0.3 m) and three different labeled masks. The first is the building mask; the second is the border mask, which is the border of the building segment with 4 pixels added outside and 3 pixels inside; and the third is the inner segment mask, which is the segment of the building diminished by 2 pixels. The architecture consists of three parallel paths, one for each mask, all starting with a U-net model. To accurately capture the overlap between the masks, all activation layers of the U-nets are copied and concatenated on each path and sent to two additional convolutional layers before the output activation layers. The method was tested with a dataset of 7563 manually delineated individual buildings of the city of Joanópolis-SP, Brazil. On this dataset, the semantic segmentation showed an overall accuracy of 97.67% and an F1-Score of 0.937 and the building individual instance segmentation showed good performance with a mean intersection over union (IoU) of 0.582 (median IoU = 0.694).

Download Full-text