Mulberry Leaf Dataset

The mulberry leaf dataset is a collection of 10 cultivars that are taken in the natural environments using DSLR cameras and smartphones. We collected the data from three regions of Thailand: northern (Chiang Mai), central (Phit- sanulok), and northeast (Nakhon Ratchasima, Buriram, and Maha Sarakham).The mulberry leaf images were captured from the natural environments. We recorded the images from different perspectives. There is a shadow that appears in the photo when holding the camera at a low position. However, when shooting from an eye-level position, the resulting image is sharp and the backlit image does not appear. All leaf images are recorded in the JPEG format. The mulberry leaf images were resized into 224 × 224 pixel resolution.The mulberry leaf dataset includes ten cultivars, which are four cultivars from Thailand: Chiang Mai 60 (386 images), Buriram 60 (500 images), Kamphaeng Saen 42 (640 images), and 761 images of mixed-breed mulberry (Chiang Mai 60 + Buriram 60). Three cultivars of Australia consist of King Red (500 images), King White (350 images), and Black Australia (637 images). Two cultivars of Taiwan consist of Taiwan Maechor (500 images) and Taiwan Strawberry (500 images). Also, 488 images of the Black Austurkey are from Turkey. This dataset contains 5,262 images in total. Note that mulberry experts advised examination of each mulberry species to label the data and avoid the errors due to the similarity pattern and shape of the leaves

Mulberry Leaf Dataset

The mulberry leaf dataset is a collection of 10 cultivars that are taken in the natural environments using DSLR cameras and smartphones. We collected the data from three regions of Thailand: northern (Chiang Mai), central (Phit- sanulok), and northeast (Nakhon Ratchasima, Buriram, and Maha Sarakham).The mulberry leaf images were captured from the natural environments. We recorded the images from different perspectives. There is a shadow that appears in the photo when holding the camera at a low position. However, when shooting from an eye-level position, the resulting image is sharp and the backlit image does not appear. All leaf images are recorded in the JPEG format. The mulberry leaf images were resized into 224 × 224 pixel resolution.The mulberry leaf dataset includes ten cultivars, which are four cultivars from Thailand: Chiang Mai 60 (386 images), Buriram 60 (500 images), Kamphaeng Saen 42 (640 images), and 761 images of mixed-breed mulberry (Chiang Mai 60 + Buriram 60). Three cultivars of Australia consist of King Red (500 images), King White (350 images), and Black Australia (637 images). Two cultivars of Taiwan consist of Taiwan Maechor (500 images) and Taiwan Strawberry (500 images). Also, 488 images of the Black Austurkey are from Turkey. This dataset contains 5,262 images in total. Note that mulberry experts advised examination of each mulberry species to label the data and avoid the errors due to the similarity pattern and shape of the leaves.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Vehicle Make Image Dataset (VMID)

The vehicle make is the brand of the vehicle and mostly the name of the company manufacturing the vehicle. People easily recognize the vehicle by seeing the logo because of its unique design and is familiar to most people. This can help a machine do the same thing. By locating and recognizing the vehicle logo, it is possible for a computer system to classify the vehicle make by analyzing the differences in each logo and figuring out how to categorize them.VMID was the collection of eleven vehicle logos in Thailand (Benz, Chevrolet, Ford, Honda, Isuzu, Mazda, MG, Mitsubishi, Nissan, Suzuki, and Toyota). The total number of images was 2,072.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Multi-language Video Subtitle Dataset

The video subtitle images were collected from 24 videos shared on Facebook and Youtube. The subtitle text included Thai and English languages, including Thai characters, Roman characters, Thai numerals, Arabic numerals, and special characters with 157 characters in total.In the data-preprocessing step, we converted all 24 videos to images and obtained 2,700 images with subtitle text. The size of the subtitle text image was 1280x720 pixels and it was stored in JPG format. Further, we generated the ground truth from 4,224 subtitle images using the labelImg program. Also, the labels were then assigned to each subtitle image. Note that the number before the label is the order of the subtitle text image.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Vehicle Type Image Dataset (Version 2): VTID2

After creating VTID, the researchers decided to extend the collection process to create another larger dataset to add further diversity to the dataset in order to avoid data overfitting. Finally, the new dataset, called "Vehicle Type Image Dataset 2 (VTID2)", consisted of 4,356 image samples that could be separated into five vehicle type classes as follows: 1,230 sedans, 1,240 pick-ups, 680 SUVs, 606 hatchbacks, and 600 other vehicle images.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Vehicle Make Image Dataset (VMID)

The vehicle make is the brand of the vehicle and mostly the name of the company manufacturing the vehicle. People easily recognize the vehicle by seeing the logo because of its unique design and is familiar to most people. This can help a machine do the same thing. By locating and recognizing the vehicle logo, it is possible for a computer system to classify the vehicle make by analyzing the differences in each logo and figuring out how to categorize them.VMID was the collection of eleven vehicle logos in Thailand (Benz, Chevrolet, Ford, Honda, Isuzu, Mazda, MG, Mitsubishi, Nissan, Suzuki, and Toyota). The total number of images was 2,072.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

EcoCropsAID

Thailand’s Economic Crops Aerial Image Dataset We introduce the novel economic crops aerial image dataset, namely the EcoCropsAID dataset. This dataset was collected in Thailand from five economic crops that were cultivated in different provinces and regions between 2014 and 2018. The aerial images of economic crops were gathered based on Agri-Map Online provided by the Ministry of Agriculture and Cooperatives and the National Electronics and Computer Technology Center (NECTEC). The Agri-Map Online is an agriculture map that all departments under the Ministry of Agriculture and Cooperatives use as an agriculture management tool. Subsequent agricultural information is accurate and up-to-date. Then, the Google Earth application was employed to capture aerial images after we selected the economic crops areas in which images were to be collected. It is quite a complex dataset because the Google Earth program used several remote imaging sensors to record the aerial images. The EcoCropsAID dataset includes five categories (rice, sugarcane, cassava, rubber, and longan) and contains 5,400 images. Each class has around 1,000 images. To prepare the aerial images of the economic crops, we recorded the image with 600 × 600 pixels and stored it in the RGB color format. The challenges of classification on the EcoCropsAID dataset are 1) many different image resolutions and colors are contained in the EcoCropsAID dataset due to the various remote imaging sensors, 2) the similarity of patterns amongst each class, for example, longan and rubber, and 3) the difference of pattern inside the same class, for example, cassava and rice

AIWR Dataset

Aerial Image Water Resources (AIWR) Dataset 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 650x650 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

Vehicle Type Image Dataset (Version 1): VTID1

The main objective for the use of an image dataset was to examine the five vehicle types of motor vehicles that were the most commonly used ones in Thailand (sedan, hatchback, pick-up, SUV, and van). The recording devices to collect the images were part of a video surveillance system located at Loei Rajabhat University in Loei province, Thailand. The collection process took place during the daytime for four weeks between July and December 2018. Two cameras were installed at the front gate of the university. However, a small number of van images was produced in the dataset compared to the number of images of the other four vehicle types. Because of this, the researchers decided to add other vehicle-type images such as those of motorcycles into the van group and changed the name of the group to "other vehicles" to increase diversity. Finally, the first dataset called "Vehicle Type Image Dataset (VTID)" had a total of 1,410 images that could be separated into vehicle types as follows; 400 sedans, 478 pick-ups, 129 SUVs, 181 hatchbacks, and 122 other vehicle images. Each image was collected using the 224x224 pixel resolution.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Multi-languages Video Subtitle Dataset

The video subtitle images were collected from 24 videos shared on Facebook and Youtube. The subtitle text included Thai and English languages, including Thai characters, Roman characters, Thai numerals, Arabic numerals, and special characters with 157 characters in total.In the data-preprocessing step, we converted all 24 videos to images and obtained 2,700 images with subtitle text. The size of the subtitle text image was 1280x720 pixels and it was stored in JPG format. Further, we generated the ground truth from 4,224 subtitle images using the labelImg program. Also, the labels were then assigned to each subtitle image. Note that the number before the label is the order of the subtitle text image.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV