TreeAI - Advancing Tree Species Identification from Aerial Images with Deep LearningData Structure for the TreeAI Database Used in the TreeAI4Species CompetitionThe dataset is organized into two distinct challenges: Object Detection and Semantic Segmentation. Below is a more detailed description of the data for each challenge:Object detectionThe data are in the COCO format, each folder contains training and validation subfolders with images and labels with the tree species ID.Tree species: 61 tree species (classes). Training: Images (.png) and Labels (.txt)Validation: Images (.png) and Labels (.txt)Images: RGB bands, 8-bit. Further details (spatial resolution, labels, etc) are given in Table 1.Labels: Prepared for object detection tasks. The number of classes varies per dataset, e.g. dataset 12_RGB_all_L has 53 classes, but species IDs are standardized across most datasets (except for 0_RGB_fL). The Latin name of the species is given for each class ID in the file named classDatasetName.xlsx.Species class: the excel file “classDatasetName.xlsx” contains 4 columns Species_ID (Sp_ID), Labels (number of labels for training and validation), and Species_Class (Latin name of the species).Masked images: The dataset with partial labels was masked, i.e. a buffer of 30 pixels (1.5 m) was created around a label, and the image was masked based on these buffers. The masked images are stored in the images_masked folder within training and validation subsets, e.g. 34_RGB_ObjDet_640_pL_b\train\images_masked.Additional filters to clean up the data:Labels at the edge: only images with labels at the edge were removed.Valid labels: images with labels that were completely within an image have been retained.  Object detection datasetTable 1. Description of the datasets for object detection included in the TreeAI database. Res. = spatial resolution. a) Fully labeled images (i.e. the image has all the trees delineated and each polygon has species information)b) Partially labeled images (i.e. the image has only some trees delineated, and each polygon has species information)No.Dataset nameRes. (cm)Training imagesValidation imagesTraining labelsValidation labelsFully labeledPartially labeled112_RGB_ObjDet_640_fL510613035391014323x 20_RGB_fL3422845150011137x 334_RGB_ObjDet_640_pLa594627142491214 x434_RGB_ObjDet_640_pLb53541011887581 x55_RGB_S_320_pL1088892688195615915 x Semantic segmentation datasetEach folder contains training and validation subfolders with images and corresponding segmentation masks, where each pixel is assigned to a specific class.Tree species: 61 tree species (classes). Training: Images (.png) and Labels (.png)Validation: Images (.png) and Labels (.png)Images: RGB bands, 8-bit, 5 cm spatial resolution. Further details are given in Table 2.Labels: Prepared for the semantic segmentation task. The number of classes varies per dataset, e.g. dataset 12_RGB_SemSegm_640_fL  has 57 classes, but the labels are standardized across both datasets. Table 2. Description of the datasets for semantic segmentation included in the TreeAI database.No.Dataset nameTraining imagesValidation imagesFully labeledPartially labeleda.12_RGB_SemSegm_640_fL1110318x b.34_RGB_SemSegm_640_pL1564446 x Steps to access the dataset and participate in the TreeAI4Species competition:Register: Access to the data will be granted upon registering for the competition, see the registration form: https://form.ethz.ch/research/tree-ai-global-database/treeai-competition.html Download the dataset: Download the competition record after registration.Test dataset: Only the participants registered for the competition will receive the test dataset.Challenges: 1) object detection and 2) semantic segmentation. Submit your DL models for evaluation by July 2025.Award: The best models for object detection and semantic segmentation will win a prize.Publication: All participants in the competition who submit the required files for evaluation will be included in the subsequent publication.License== CC BY-NC-ND (Attribution-NonCommercial-NoDerivatives) == Dear user,We appreciate your interest in the TreeAI4Species Competition:  https://form.ethz.ch/research/tree-ai-global-database.html DATA ANALYSIS AND PUBLICATIONThe TreeAI database is released under a variant of the CC BY-NC-ND license. This database is created for the TreeAI4Species data science competition. It is not permitted to pass on the data or the characteristics directly derived from it to third parties. Written consent from the data supplier is required for use for any other purpose. LIABILITYThe data are based on the current state of existing scientific knowledge. However, there is no liability for the completeness. This is the second version of the database, and we might improve the tree annotations and include new tree species in future versions.The data can only be used for the purpose described by the provider. ------------------------------------------------------ETH ZürichDr. Mirela Beloiu SchwenkeInstitute of Terrestrial Ecosystems Department of Environmental Systems Science, CHN K75Universitätstrasse 16, 8092 Zürich, [email protected]

TreeAI - Advancing Tree Species Identification from Aerial Images with Deep LearningData Structure for the TreeAI Database Used in the TreeAI4Species CompetitionThe dataset is organized into two distinct challenges: Object Detection and Semantic Segmentation. Below is a more detailed description of the data for each challenge:Object detectionThe data are in the COCO format, each folder contains training and validation subfolders with images and labels with the tree species ID.Tree species: 61 tree species (classes). Training: Images (.png) and Labels (.txt)Validation: Images (.png) and Labels (.txt)Images: RGB bands, 8-bit. Further details (spatial resolution, labels, etc) are given in Table 1.Labels: Prepared for object detection tasks. The number of classes varies per dataset, e.g. dataset 12_RGB_all_L has 53 classes, but species IDs are standardized across most datasets (except for 0_RGB_fL). The Latin name of the species is given for each class ID in the file named classDatasetName.xlsx.Species class: the excel file “classDatasetName.xlsx” contains 4 columns Species_ID (Sp_ID), Labels (number of labels for training and validation), and Species_Class (Latin name of the species).Masked images: The dataset with partial labels was masked, i.e. a buffer of 30 pixels (1.5 m) was created around a label, and the image was masked based on these buffers. The masked images are stored in the images_masked folder within training and validation subsets, e.g. 34_RGB_ObjDet_640_pL_b\train\images_masked.Additional filters to clean up the data:Labels at the edge: only images with labels at the edge were removed.Valid labels: images with labels that were completely within an image have been retained.  Object detection datasetTable 1. Description of the datasets for object detection included in the TreeAI database. Res. = spatial resolution. a) Fully labeled images (i.e. the image has all the trees delineated and each polygon has species information)b) Partially labeled images (i.e. the image has only some trees delineated, and each polygon has species information)No.Dataset nameRes. (cm)Training imagesValidation imagesTraining labelsValidation labelsFully labeledPartially labeled112_RGB_ObjDet_640_fL510613035391014323x 20_RGB_fL3422845150011137x 334_RGB_ObjDet_640_pLa594627142491214 x434_RGB_ObjDet_640_pLb53541011887581 x55_RGB_S_320_pL1088892688195615915 x Semantic segmentation datasetEach folder contains training and validation subfolders with images and corresponding segmentation masks, where each pixel is assigned to a specific class.Tree species: 61 tree species (classes). Training: Images (.png) and Labels (.png)Validation: Images (.png) and Labels (.png)Images: RGB bands, 8-bit, 5 cm spatial resolution. Further details are given in Table 2.Labels: Prepared for the semantic segmentation task. The number of classes varies per dataset, e.g. dataset 12_RGB_SemSegm_640_fL  has 57 classes, but the labels are standardized across both datasets. Table 2. Description of the datasets for semantic segmentation included in the TreeAI database.No.Dataset nameTraining imagesValidation imagesFully labeledPartially labeleda.12_RGB_SemSegm_640_fL1110318x b.34_RGB_SemSegm_640_pL1564446 x Steps to access the dataset and participate in the TreeAI4Species competition:Register: Access to the data will be granted upon registering for the competition, see the registration form: https://form.ethz.ch/research/tree-ai-global-database/treeai-competition.html Download the dataset: Download the competition record after registration.Test dataset: Only the participants registered for the competition will receive the test dataset.Challenges: 1) object detection and 2) semantic segmentation. Submit your DL models for evaluation by July 2025.Award: The best models for object detection and semantic segmentation will win a prize.Publication: All participants in the competition who submit the required files for evaluation will be included in the subsequent publication.License== CC BY-NC-ND (Attribution-NonCommercial-NoDerivatives) == Dear user,We appreciate your interest in the TreeAI4Species Competition:  https://form.ethz.ch/research/tree-ai-global-database.html DATA ANALYSIS AND PUBLICATIONThe TreeAI database is released under a variant of the CC BY-NC-ND license. This database is created for the TreeAI4Species data science competition. It is not permitted to pass on the data or the characteristics directly derived from it to third parties. Written consent from the data supplier is required for use for any other purpose. LIABILITYThe data are based on the current state of existing scientific knowledge. However, there is no liability for the completeness. This is the second version of the database, and we might improve the tree annotations and include new tree species in future versions.The data can only be used for the purpose described by the provider. ------------------------------------------------------ETH ZürichDr. Mirela Beloiu SchwenkeInstitute of Terrestrial Ecosystems Department of Environmental Systems Science, CHN K75Universitätstrasse 16, 8092 Zürich, [email protected]

TreeAI - Advancing Tree Species Identification from Aerial Images with Deep LearningData Structure for the TreeAI Database Used in the TreeAI4Species CompetitionThe data are in the COCO format, each folder contains training and validation subfolders with images and labels with the tree species ID.Training: Images (.png) and Labels (.txt)Validation: Images (.png) and Labels (.txt)Images: RGB bands, 8-bit, chip size 640 x 640 pixels = 32 x 32 m, 5 cm pixel spatial resolution.Labels: labels are prepared for object detection tasks, the number of classes varies per dataset, e.g. dataset 12_RGB_all_L has 53 classes, and the Latin name of the species is given for each class ID in the file named classDatasetName.xlsx.Species class: classDatasetName.xlsx contains 3 columns Species_ID, Labels (number of labels), and Species_Class (Latin name of the species).Masked images: The data set with partial labels was masked, i.e. a buffer of 30 pixels was created around a label, and the image was masked based on these buffers, e.g. 34_RGB_all_L_PascalVoc_640Mask.Additional filters to clean up the data:Labels at the edge: only images with labels at the edge were removed.Valid labels: images with labels that were completely within an image have been retained.  Table 1. Description of the datasets included in the TreeAI database.a) Fully labeled images (i.e. the image has all the trees delineated and each polygon has species information)b) Partially labeled images (i.e. the image has only some trees delineated, and each polygon has species information)No.Dataset nameTraining imagesValidation imagesFully labeledPartially labeled112_RGB5cm_FullyLabeled1066304x 2ObjectDetection_TreeSpecies42284x 334_RGB_all_L_PascalVoc_640Mask951272 x434_RGB_PartiallyLabeled640917262 x Steps to access the dataset and participate in the TreeAI4Species competition:Register: Access to the data will be granted upon registering for the competition, see the registration form: https://form.ethz.ch/research/tree-ai-global-database/treeai-competition.html Request the dataset: Download the competition record after registration by requesting it. Enter your full name, purpose e.g. accept the TreeAI4Species data license, affiliation, and the country of affiliation in the request. This allows us to check whether you are already registered.Test dataset: Only the participants registered for the competition will receive the test dataset.Submit your DL models for evaluation by June 2025.Award: The best models win a prize.Publication: All participants in the competition who submit the required files for evaluation will be included in the subsequent publication.License== CC BY-NC-ND (Attribution-NonCommercial-NoDerivatives) == Dear user,We appreciate your interest in the TreeAI4Species Competition:  https://form.ethz.ch/research/tree-ai-global-database.html DATA ANALYSIS AND PUBLICATIONThe TreeAI database is released under a variant of the CC BY-NC-ND license. This database is confidential and can be used only for the TreeAI4Species data science competition. It is not permitted to pass on the data or the characteristics directly derived from it to third parties. Written consent from the data supplier is required for use for any other purpose. LIABILITYThe data are based on the current state of existing scientific knowledge. However, there is no liability for the completeness. This is the first version of the database, and we plan to improve the tree annotations and include new tree species. Therefore, another version will be released in the future.The data can only be used for the purpose described by the user when requesting the data. ------------------------------------------------------ETH ZürichDr. Mirela Beloiu SchwenkeInstitute of Terrestrial Ecosystems Department of Environmental Systems Science, CHN K75Universitätstrasse 16, 8092 Zürich, [email protected]