Dataset

Mulberry Leaf Dataset

Thipwimon Chompookham and Olarik Surinta

Cite this dataset: T. Chompookham and O. Surinta (2021). Ensemble Methods with Deep Convolutional Neural Networks for Plant Leaf Recognition. ICIC Express Letters, 15(6), 553-565.

Illustration of the ten mulberry leaf cultivars including (A) King Red, (B) King White, (C) Taiwan Maechor, (D) Taiwan Strawberry, (E) Black Austurkey, (F) Black Australia, (G) Chiang Mai 60, (H) Buriram 60, (I) Kamphaeng Saen 42, and (J) Mixed Chiang Mai 60+Buriram 60

AIWR Dataset

Aerial Image Water Resources Dataset

Sangdaow Noppitak and Olarik Surinta

According to the standard of land use code by fundamental geographic data set: FGDS, Thailand land use classification requires an analysis and transformation of satellite images data together with field survey data. In this article, researchers studied only land use in water bodies. The water bodies in this research can be divided into 2 levels: natural body of water (W1) artificial body of (W2) water.

The aerial image data used in this research was 1:50 meters. Every aerial image had 650×650 pixels. Those images included water bodies type W1 and W2. Ground truth of all aerial images was set for before sending it to be analyzed and interpreted by remote sensing experts. This assured that the water bodies groupings were correct. An example of ground truth, which has been checked by experts. Ground truth has been used in learning the algorithm in deep learning mode and also used in further evaluation.

The aerial images used in the experiment consists of water body: types W1 and W2. Aerial image water resources dataset, AIWR has 800 images. Data were chosen at random and divided into 3 sections: training, validation, and test set with ratio 8:1:1. Therefore, 640 aerial images were used for learning and creating the model, 80 images were used for validation, and the remaining 80 images were used for test.

Link to download AIWR dataset : https://data.mendeley.com/datasets/d73mpc529b/2

Cite this dataset: S. Noppitak, S. Gonwirat, and O. Surinta (2020). Instance Segmentation of Water Body from Aerial Image using Mask Region-based Convolutional Neural Network, in Information Science and System (ICISS), The 3rd International Conference on, 61-66.  https://doi.org/10.1145/3388176.3388184

Example of aerial images. a) Water bodies W1 and W2 b) ground truth of water resources.

EcoCropsAID Dataset

Thailand’s Economic Crops Aerial Image Dataset

Sangdaow Noppitak and Olarik Surinta

Cite this dataset: S. Noppitak and O. Surinta (2021). Ensemble Convolutional Network Architectures for Land Use Classification in Economic Crops Aerial Images. ICIC Express Letters, 15(6), 531-543.

Example of economic crops aerial images: (A) Cassava, (B) longan, (C) rice, (D) rubber, and (E) sugarcane

VTID1 Dataset

Vehicle Type Image Dataset (Version 1)

Narong Boonsirisumpun and Olarik Surinta

Link to download VTID1 dataset : https://data.mendeley.com/datasets/r7bthvstxw/1

Cite this dataset: N. Boonsirisumpun and O. Surinta (2022). Fast and Accurate Deep Learning Architecture on Vehicle Type Recognition, Current Applied Science and Technology, 22(1) (January-February 2022), 1-16. https://li01.tci-thaijo.org/index.php/cast/article/view/250863 

Example of VTID1 dataset collected in four different views (front, back, left, and right): a) Sedan, b) Hatchback, c) Pick-up, d) SUV, e) Other vehicles

VTID2 Dataset

Vehicle Type Image Dataset (Version 2)

Narong Boonsirisumpun and Olarik Surinta

Cite this dataset: N. Boonsirisumpun and O. Surinta (2022). Fast and Accurate Deep Learning Architecture on Vehicle Type Recognition, Current Applied Science and Technology, 22(1) (January-February 2022), 1-16. https://li01.tci-thaijo.org/index.php/cast/article/view/250863 

Multi-language Video Subtitle Dataset

Thanadol Singkhornart and Olarik Surinta

Link to download Multi-language Video Subtitle dataset : https://data.mendeley.com/datasets/gj8d88h2g3/2

Cite this dataset: T. Singkhornart and O. Surinta (2022). Multi-Language Video Subtitle Recognition with Convolutional Neural Network and Long Short-Term Memory Networks, ICIC Express Letters, 16(6)..

Example of Multi-language Video Subtitle Dataset

Vehicle Make Image Dataset (VMID)

Vehicle Make Image Dataset (VMID)

Narong Boonsirisumpun and Olarik Surinta

Cite this dataset: Boonsirisumpun, N. and Surinta, O. (2022). Ensemble Multiple CNNs methods with partial Training Set for Vehicle Image ClassificationScience, Engineering and Health Studies, 16, 220200011. doi: https://doi.org/10.14456/sehs.2022.12

Count from October 23, 2021

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