SurfaceView: Seamless and Tile-Based Orthomosaics Using Millions of Street-Level Images From Vehicle-Mounted Cameras

Tanathong, Supannee; Smith, William A. P.; Remde, Stephen

doi:10.1109/tits.2020.3036928

Cited by 2 publications

(2 citation statements)

References 78 publications

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“…Judging from (1), the first step is to use the image pixels taken by the UAV by equating the shooting motion of the UAV so that the images will be arranged in order. Then, the second step is to determine the spatial transformation and simultaneously wrap the target image into the reference image [8].…”

Section: Orthomosaics Stitching Mapsmentioning

confidence: 99%

Classification of tea plantation using orthomosaics stitching maps from aerial images based on CNN

Wirabudi¹,

Fachrurrozi

2023

J.INFOTEL

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In Indonesia, Tea is an important economic crop that is widely grown, and in many countries, accurate mapping of tea plantations is essential for the operation, management, and monitoring of the growth and development of the tea industry. We propose a classification of tea plantations using orthomosaics from aerial images based on the Convolutional Neural Network (CNN) which identifies the condition of the tea plantations with the parameters observed, namely the condition of the tea leaves, estimated yields achieved, and monitoring of treeless areas caused by tree death. In this study, we took a sample of 20 hectares. We classify images based on maps generated by drones in previous studies. Image segmentation is performed to maintain image objects, while an enhanced CNN model is used to extract deep image features. To get complete results, this study uses UAV (Unmanned Aerial Vehicle) imagery as the basis for the map, which is then combined or stacked into one image. The results of the images that are used as maps undergo image classification, where the information contained in the map is mapped and divided according to its type. The area of the tea plantations sampled is 20 ha, and the threshold for the image captured by the UAV is 5% of the total area captured, which is around 1 ha. If the image created by the UAV has an error of more than 5%, then the image does not meet the classification requirements. We determine this margin of error based on the performance of the drone camera capture when capturing Fig. 2, and the resolution used is 4096 x 2160 for each image captured by the drone. We conclude that the proposed method for mapping tea plantations using ultra-high resolution remote sensing imagery is effective and has great potential for mapping tea plantations in areas such as the development of drone aerial photography methods for tea plantations based on image classification for forecasting. tea plantations Image stitching can be used to improve the monitoring of tea plantations and predict harvest time using a classification process. The tea garden map has 5 types of information categorized by harvest time, medium leaf tea, milled tea, tea, and old tea. The success of image recognition shows the error matrix data by testing 123 random points spread over the map, of which 113 random points were identified with an average accuracy of 91.87%, this value is of course very good and exceeds the specified threshold of 75%. When using this method, an error occurs that the colors of similar pixels cannot be distinguished, resulting in an incorrect detection. In addition, the image stitching method using the orthomosaics method has succeeded in performing image stitching and can be well applied to classification using the CNN approach.

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Section: Orthomosaics Stitching Mapsmentioning

confidence: 99%

Classification of tea plantation using orthomosaics stitching maps from aerial images based on CNN

Wirabudi¹,

Fachrurrozi

2023

J.INFOTEL

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“…The rapid development of cities requires research on all aspects of urbanization. Existing large-scale urban mapping research includes urban building type mapping [4]- [6], urban functional zone mapping [7], [8], local climate zone mapping [9], urban building height mapping [10], [11], urban region mapping [12], [13], and more. Urban building mapping includes many small-scale studies but few large-scale ones.…”

Section: A Large-scale Urban Mappingmentioning

confidence: 99%

A Large-Scale Mapping Scheme for Urban Building From Gaofen-2 Images Using Deep Learning and Hierarchical Approach

Wang¹,

He²,

Yin³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Urban buildings are essential components of cities and an indispensable source of urban geographic information. While there are many research efforts focused on urban buildings extraction, there are few studies on large-scale urban building mapping based on satellite images. In this research, a large-scale urban building mapping scheme based on Gaofen-2 satellite (GF-2) images is proposed based on a hierarchical approach. In this hierarchical approach, urban buildings are regarded as a mixture of dense low-rise buildings (DLB) and sparse independent buildings (SIB) stacked in space, which are extracted by a semantic segmentation model and an instance segmentation model, respectively. In this study, GF-2 images and OpenStreetMap data were used to extract DLB using U 2 -Net with focalloss. GF-2 images were used to extract SIB using an improved CenterMask model with a deformable convolution network and a spatial coordinate attention module. The main urban area within the 5th ring road of Beijing was selected as the study area. With the trained model, the GF-2 image tiles of Beijing input into the models to first derive coarse maps of DLB and SIB. Post-processing optimization was performed after combining the maps. The accuracy assessment shows that the overall accuracy of large-scale urban building mapping using the hierarchical approach proposed in this paper reaches 91.5%, which is 4.8% higher than with a traditional method. Overall, the hierarchical approach proposed in this paper is effective in large-scale urban building mapping and provides new application opportunities.

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SurfaceView: Seamless and Tile-Based Orthomosaics Using Millions of Street-Level Images From Vehicle-Mounted Cameras

Cited by 2 publications

References 78 publications

Classification of tea plantation using orthomosaics stitching maps from aerial images based on CNN

Classification of tea plantation using orthomosaics stitching maps from aerial images based on CNN

A Large-Scale Mapping Scheme for Urban Building From Gaofen-2 Images Using Deep Learning and Hierarchical Approach

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