Automatic Mapping of Thermokarst Landforms from Remote Sensing Images Using Deep Learning: A Case Study in the Northeastern Tibetan Plateau

Thawing of ice-rich permafrost causes thermokarst landforms on the ground surface. Obtaining the distribution of thermokarst landforms is a prerequisite for understanding permafrost degradation and carbon exchange at local and regional scales. However, because of their diverse types and characteristics, it is challenging to map thermokarst landforms from remote sensing images. We conducted a case study towards automatically mapping a type of thermokarst landforms (i.e., thermo-erosion gullies) in a local area in the northeastern Tibetan Plateau from high-resolution images by the use of deep learning. In particular, we applied the DeepLab algorithm (based on Convolutional Neural Networks) to a 0.15-m-resolution Digital Orthophoto Map (created using aerial photographs taken by an Unmanned Aerial Vehicle). Here, we document the detailed processing flow with key steps including preparing training data, fine-tuning, inference, and post-processing. Validating against the field measurements and manual digitizing results, we obtained an F1 score of 0.74 (precision is 0.59 and recall is 1.0), showing that the proposed method can effectively map small and irregular thermokarst landforms. It is potentially viable to apply the designed method to mapping diverse thermokarst landforms in a larger area where high-resolution images and training data are available.

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Efficient Patch-Wise Semantic Segmentation for Large-Scale Remote Sensing Images

Sensors ◽

10.3390/s18103232 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3232 ◽

Cited By ~ 17

Author(s):

Yan Liu ◽

Qirui Ren ◽

Jiahui Geng ◽

Meng Ding ◽

Jiangyun Li

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Large Scale ◽

Semantic Segmentation ◽

Remote Sensing Image ◽

Training Data ◽

Land Resources ◽

Remote Sensing Images ◽

Training Strategy ◽

The Impact

Efficient and accurate semantic segmentation is the key technique for automatic remote sensing image analysis. While there have been many segmentation methods based on traditional hand-craft feature extractors, it is still challenging to process high-resolution and large-scale remote sensing images. In this work, a novel patch-wise semantic segmentation method with a new training strategy based on fully convolutional networks is presented to segment common land resources. First, to handle the high-resolution image, the images are split as local patches and then a patch-wise network is built. Second, training data is preprocessed in several ways to meet the specific characteristics of remote sensing images, i.e., color imbalance, object rotation variations and lens distortion. Third, a multi-scale training strategy is developed to solve the severe scale variation problem. In addition, the impact of conditional random field (CRF) is studied to improve the precision. The proposed method was evaluated on a dataset collected from a capital city in West China with the Gaofen-2 satellite. The dataset contains ten common land resources (Grassland, Road, etc.). The experimental results show that the proposed algorithm achieves 54.96% in terms of mean intersection over union (MIoU) and outperforms other state-of-the-art methods in remote sensing image segmentation.

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Monitoring of Urban Impervious Surfaces Using Time Series of High-Resolution Remote Sensing Images in Rapidly Urbanized Areas: A Case Study of Shenzhen

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2018.2804440 ◽

2018 ◽

Vol 11 (8) ◽

pp. 2692-2708 ◽

Cited By ~ 11

Author(s):

Tao Zhang ◽

Xin Huang

Keyword(s):

Remote Sensing ◽

Time Series ◽

High Resolution ◽

Impervious Surfaces ◽

Remote Sensing Images ◽

Urbanized Areas ◽

Urban Impervious Surfaces

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Road Extraction from Very-High-Resolution Remote Sensing Images via a Nested SE-Deeplab Model

Remote Sensing ◽

10.3390/rs12182985 ◽

2020 ◽

Vol 12 (18) ◽

pp. 2985 ◽

Cited By ~ 1

Author(s):

Yeneng Lin ◽

Dongyun Xu ◽

Nan Wang ◽

Zhou Shi ◽

Qiuxiao Chen

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

High Resolution ◽

Network Models ◽

Road Extraction ◽

Remote Sensing Images ◽

Proposed Model ◽

Wide Range ◽

Network Modules ◽

Very High

Automatic road extraction from very-high-resolution remote sensing images has become a popular topic in a wide range of fields. Convolutional neural networks are often used for this purpose. However, many network models do not achieve satisfactory extraction results because of the elongated nature and varying sizes of roads in images. To improve the accuracy of road extraction, this paper proposes a deep learning model based on the structure of Deeplab v3. It incorporates squeeze-and-excitation (SE) module to apply weights to different feature channels, and performs multi-scale upsampling to preserve and fuse shallow and deep information. To solve the problems associated with unbalanced road samples in images, different loss functions and backbone network modules are tested in the model’s training process. Compared with cross entropy, dice loss can improve the performance of the model during training and prediction. The SE module is superior to ResNext and ResNet in improving the integrity of the extracted roads. Experimental results obtained using the Massachusetts Roads Dataset show that the proposed model (Nested SE-Deeplab) improves F1-Score by 2.4% and Intersection over Union by 2.0% compared with FC-DenseNet. The proposed model also achieves better segmentation accuracy in road extraction compared with other mainstream deep-learning models including Deeplab v3, SegNet, and UNet.

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Vehicle Detection in High Resolution Satellite Remote Sensing Images Based on Deep Learning

IEEE Access ◽

10.1109/access.2020.3017894 ◽

2020 ◽

Vol 8 ◽

pp. 153394-153402

Author(s):

Qulin Tan ◽

Juan Ling ◽

Jun Hu ◽

Xiaochun Qin ◽

Jiping Hu

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

High Resolution ◽

Satellite Remote Sensing ◽

Vehicle Detection ◽

Remote Sensing Images

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A Y-Net deep learning method for road segmentation using high-resolution visible remote sensing images

Remote Sensing Letters ◽

10.1080/2150704x.2018.1557791 ◽

2019 ◽

Vol 10 (4) ◽

pp. 381-390 ◽

Cited By ~ 12

Author(s):

Ye Li ◽

Lele Xu ◽

Jun Rao ◽

Lili Guo ◽

Zhen Yan ◽

...

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

High Resolution ◽

Learning Method ◽

Remote Sensing Images ◽

Road Segmentation

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A Novel Intelligent Classification Method for Urban Green Space Based on High-Resolution Remote Sensing Images

Remote Sensing ◽

10.3390/rs12223845 ◽

2020 ◽

Vol 12 (22) ◽

pp. 3845

Author(s):

Zhiyu Xu ◽

Yi Zhou ◽

Shixin Wang ◽

Litao Wang ◽

Feng Li ◽

...

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

High Resolution ◽

Classification Accuracy ◽

Green Space ◽

Urban Green Space ◽

Classification Method ◽

Deciduous Trees ◽

Remote Sensing Images ◽

Urban Green

The real-time, accurate, and refined monitoring of urban green space status information is of great significance in the construction of urban ecological environment and the improvement of urban ecological benefits. The high-resolution technology can provide abundant information of ground objects, which makes the information of urban green surface more complicated. The existing classification methods are challenging to meet the classification accuracy and automation requirements of high-resolution images. This paper proposed a deep learning classification method for urban green space based on phenological features constraints in order to make full use of the spectral and spatial information of green space provided by high-resolution remote sensing images (GaoFen-2) in different periods. The vegetation phenological features were added as auxiliary bands to the deep learning network for training and classification. We used the HRNet (High-Resolution Network) as our model and introduced the Focal Tversky Loss function to solve the sample imbalance problem. The experimental results show that the introduction of phenological features into HRNet model training can effectively improve urban green space classification accuracy by solving the problem of misclassification of evergreen and deciduous trees. The improvement rate of F1-Score of deciduous trees, evergreen trees, and grassland were 0.48%, 4.77%, and 3.93%, respectively, which proved that the combination of vegetation phenology and high-resolution remote sensing image can improve the results of deep learning urban green space classification.

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Discussion on Remote Dynamic Sensing Monitoring Method for Land in Regions under Rapid Economic Development

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.518-523.5788 ◽

2012 ◽

Vol 518-523 ◽

pp. 5788-5792

Author(s):

Zheng Dong Xie ◽

Jian Zhang ◽

Bu Zhuo Peng

Keyword(s):

Remote Sensing ◽

Land Use ◽

High Resolution ◽

Property Right ◽

Remote Sensing Images ◽

Monitoring Method ◽

Nanjing City ◽

Platform System ◽

First Time

The paper was supported by The Second Land Investigation Item and took Nanjing city, Jiangsu Province as a case study. The research of the theory, technique and application for land use investigation was achieved by the high-resolution remote sensing images for application, designed a set of technique of land use investigation for land property right management. The database and platform system were established to carry out the dynamic management of land use. Based on the summarization of the correlative studies, The paper designed a set of technique of land investigation for land property right management and also designed the technical process, dealt with the remote sensing images, detected the changed information, classified the land, investigated the land property right and established the database to serve for the management of land property right. And it has been successfully used in Nanjing. It’s unique to use the high-resolution remote sensing images by QuichBird for the scale of 1:5000 in land use investigation in area cities which is also the first time in Nanjing City.

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DEEP LEARNING BASED ROOF TYPE CLASSIFICATION USING VERY HIGH RESOLUTION AERIAL IMAGERY

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b3-2021-55-2021 ◽

2021 ◽

Vol XLIII-B3-2021 ◽

pp. 55-60

Author(s):

M. Buyukdemircioglu ◽

R. Can ◽

S. Kocaman

Keyword(s):

Deep Learning ◽

High Resolution ◽

Urban Areas ◽

Image Features ◽

Training Data ◽

Fine Tuning ◽

Computer Hardware ◽

Geographical Information ◽

Training Dataset ◽

Very High

Abstract. Automatic detection, segmentation and reconstruction of buildings in urban areas from Earth Observation (EO) data are still challenging for many researchers. Roof is one of the most important element in a building model. The three-dimensional geographical information system (3D GIS) applications generally require the roof type and roof geometry for performing various analyses on the models, such as energy efficiency. The conventional segmentation and classification methods are often based on features like corners, edges and line segments. In parallel to the developments in computer hardware and artificial intelligence (AI) methods including deep learning (DL), image features can be extracted automatically. As a DL technique, convolutional neural networks (CNNs) can also be used for image classification tasks, but require large amount of high quality training data for obtaining accurate results. The main aim of this study was to generate a roof type dataset from very high-resolution (10 cm) orthophotos of Cesme, Turkey, and to classify the roof types using a shallow CNN architecture. The training dataset consists 10,000 roof images and their labels. Six roof type classes such as flat, hip, half-hip, gable, pyramid and complex roofs were used for the classification in the study area. The prediction performance of the shallow CNN model used here was compared with the results obtained from the fine-tuning of three well-known pre-trained networks, i.e. VGG-16, EfficientNetB4, ResNet-50. The results show that although our CNN has slightly lower performance expressed with the overall accuracy, it is still acceptable for many applications using sparse data.

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