The Data Labeling Solutions and Services market is experiencing robust growth, driven by the escalating demand for high-quality training data in the artificial intelligence (AI) and machine learning (ML) sectors. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, reaching approximately $75 billion by 2033. This expansion is fueled by several key factors. Firstly, the increasing adoption of AI across diverse industries, including automotive, healthcare, and finance, necessitates vast amounts of accurately labeled data for model training and improvement. Secondly, advancements in deep learning algorithms and the emergence of sophisticated data annotation tools are streamlining the labeling process, boosting efficiency and reducing costs. Finally, the growing availability of diverse data sources, coupled with the rise of specialized data labeling companies, is further contributing to market growth. Despite these positive trends, the market faces some challenges. The high cost associated with data annotation, particularly for complex datasets requiring specialized expertise, can be a barrier for smaller businesses. Ensuring data quality and consistency across large-scale projects remains a critical concern, necessitating robust quality control measures. Furthermore, addressing data privacy and security issues is essential to maintain ethical standards and build trust within the market. The market segmentation by type (text, image/video, audio) and application (automotive, government, healthcare, financial services, etc.) presents significant opportunities for specialized service providers catering to niche needs. Competition is expected to intensify as new players enter the market, focusing on innovative solutions and specialized services.

The global Data Labeling Solution and Services market is experiencing robust growth, driven by the increasing adoption of artificial intelligence (AI) and machine learning (ML) across diverse sectors. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, reaching an estimated market value of $70 billion by 2033. This significant expansion is fueled by the burgeoning need for high-quality training data to enhance the accuracy and performance of AI models. Key growth drivers include the expanding application of AI in various industries like automotive (autonomous vehicles), healthcare (medical image analysis), and financial services (fraud detection). The increasing availability of diverse data types (text, image/video, audio) further contributes to market growth. However, challenges such as the high cost of data labeling, data privacy concerns, and the need for skilled professionals to manage and execute labeling projects pose certain restraints on market expansion. Segmentation by application (automotive, government, healthcare, financial services, others) and data type (text, image/video, audio) reveals distinct growth trajectories within the market. The automotive and healthcare sectors currently dominate, but the government and financial services segments are showing promising growth potential. The competitive landscape is marked by a mix of established players and emerging startups. Companies like Amazon Mechanical Turk, Appen, and Labelbox are leading the market, leveraging their expertise in crowdsourcing, automation, and specialized data labeling solutions. However, the market shows strong potential for innovation, particularly in the development of automated data labeling tools and the expansion of services into niche areas. Regional analysis indicates strong market penetration in North America and Europe, driven by early adoption of AI technologies and robust research and development efforts. However, Asia-Pacific is expected to witness significant growth in the coming years fueled by rapid technological advancements and a rising demand for AI solutions. Further investment in R&D focused on automation, improved data security, and the development of more effective data labeling methodologies will be crucial for unlocking the full potential of this rapidly expanding market.

According to Cognitive Market Research, the global Ai Training Data market size is USD 1865.2 million in 2023 and will expand at a compound annual growth rate (CAGR) of 23.50% from 2023 to 2030.

The demand for Ai Training Data is rising due to the rising demand for labelled data and diversification of AI applications.
Demand for Image/Video remains higher in the Ai Training Data market.
The Healthcare category held the highest Ai Training Data market revenue share in 2023.
North American Ai Training Data will continue to lead, whereas the Asia-Pacific Ai Training Data market will experience the most substantial growth until 2030.

Market Dynamics of AI Training Data Market

Key Drivers of AI Training Data Market

Rising Demand for Industry-Specific Datasets to Provide Viable Market Output

A key driver in the AI Training Data market is the escalating demand for industry-specific datasets. As businesses across sectors increasingly adopt AI applications, the need for highly specialized and domain-specific training data becomes critical. Industries such as healthcare, finance, and automotive require datasets that reflect the nuances and complexities unique to their domains. This demand fuels the growth of providers offering curated datasets tailored to specific industries, ensuring that AI models are trained with relevant and representative data, leading to enhanced performance and accuracy in diverse applications.

In July 2021, Amazon and Hugging Face, a provider of open-source natural language processing (NLP) technologies, have collaborated. The objective of this partnership was to accelerate the deployment of sophisticated NLP capabilities while making it easier for businesses to use cutting-edge machine-learning models. Following this partnership, Hugging Face will suggest Amazon Web Services as a cloud service provider for its clients.

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Advancements in Data Labelling Technologies to Propel Market Growth

The continuous advancements in data labelling technologies serve as another significant driver for the AI Training Data market. Efficient and accurate labelling is essential for training robust AI models. Innovations in automated and semi-automated labelling tools, leveraging techniques like computer vision and natural language processing, streamline the data annotation process. These technologies not only improve the speed and scalability of dataset preparation but also contribute to the overall quality and consistency of labelled data. The adoption of advanced labelling solutions addresses industry challenges related to data annotation, driving the market forward amidst the increasing demand for high-quality training data.

In June 2021, Scale AI and MIT Media Lab, a Massachusetts Institute of Technology research centre, began working together. To help doctors treat patients more effectively, this cooperation attempted to utilize ML in healthcare.

www.ncbi.nlm.nih.gov/pmc/articles/PMC7325854/

Restraint Factors Of AI Training Data Market

Data Privacy and Security Concerns to Restrict Market Growth

A significant restraint in the AI Training Data market is the growing concern over data privacy and security. As the demand for diverse and expansive datasets rises, so does the need for sensitive information. However, the collection and utilization of personal or proprietary data raise ethical and privacy issues. Companies and data providers face challenges in ensuring compliance with regulations and safeguarding against unauthorized access or misuse of sensitive information. Addressing these concerns becomes imperative to gain user trust and navigate the evolving landscape of data protection laws, which, in turn, poses a restraint on the smooth progression of the AI Training Data market.

How did COVID–19 impact the Ai Training Data market?

The COVID-19 pandemic has had a multifaceted impact on the AI Training Data market. While the demand for AI solutions has accelerated across industries, the availability and collection of training data faced challenges. The pandemic disrupted traditional data collection methods, leading to a slowdown in the generation of labeled datasets due to restrictions on physical operations. Simultaneously, the surge in remote work and the increased reliance on AI-driven technologies for various applications fueled the need for diverse and relevant training data. This duali...

The AI data labeling solutions market is experiencing robust growth, driven by the increasing demand for high-quality data to train and improve the accuracy of artificial intelligence algorithms. The market size in 2025 is estimated at $5 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033. This significant expansion is fueled by several key factors. The proliferation of AI applications across diverse sectors, including automotive, healthcare, and finance, necessitates vast amounts of labeled data. Cloud-based solutions are gaining prominence due to their scalability, cost-effectiveness, and accessibility. Furthermore, advancements in data annotation techniques and the emergence of specialized AI data labeling platforms are contributing to market expansion. However, challenges such as data privacy concerns, the need for highly skilled professionals, and the complexities of handling diverse data formats continue to restrain market growth to some extent. The market segmentation reveals that the cloud-based solutions segment is expected to dominate due to its inherent advantages over on-premise solutions. In terms of application, the automotive sector is projected to exhibit the fastest growth, driven by the increasing adoption of autonomous driving technology and advanced driver-assistance systems (ADAS). The healthcare industry is also a major contributor, with the rise of AI-powered diagnostic tools and personalized medicine driving demand for accurate medical image and data labeling. Geographically, North America currently holds a significant market share, but the Asia-Pacific region is poised for rapid growth owing to increasing investments in AI and technological advancements. The competitive landscape is marked by a diverse range of established players and emerging startups, fostering innovation and competition within the market. The continued evolution of AI and its integration across various industries ensures the continued expansion of the AI data labeling solution market in the coming years.

The Image Data Labeling Service market is expected to experience significant growth over the next decade, driven by the increasing demand for annotated data for artificial intelligence (AI) applications. The market is expected to grow from USD XXX million in 2025 to USD XXX million by 2033, at a CAGR of XX%. The growth of the market is attributed to the growing adoption of AI in various industries, including IT, automotive, healthcare, and financial services. The growing use of computer vision and machine learning algorithms for tasks such as object detection, image classification, and facial recognition has led to a surge in demand for annotated data. Image data labeling services provide the labeled data that is essential for training these algorithms. The market is expected to be further driven by the increasing availability of cloud-based services and the adoption of automation tools for image data labeling. Additionally, the growing awareness of the importance of data quality for AI applications is expected to drive the adoption of image data labeling services.

The Data Annotation and Collection Services market is experiencing robust growth, driven by the increasing adoption of artificial intelligence (AI) and machine learning (ML) across diverse sectors. The market, estimated at $10 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, reaching approximately $45 billion by 2033. This significant expansion is fueled by several key factors. The surge in autonomous driving initiatives necessitates high-quality data annotation for training self-driving systems, while the burgeoning smart healthcare sector relies heavily on annotated medical images and data for accurate diagnoses and treatment planning. Similarly, the growth of smart security systems and financial risk control applications demands precise data annotation for improved accuracy and efficiency. Image annotation currently dominates the market, followed by text annotation, reflecting the widespread use of computer vision and natural language processing. However, video and voice annotation segments are showing rapid growth, driven by advancements in AI-powered video analytics and voice recognition technologies. Competition is intense, with both established technology giants like Alibaba Cloud and Baidu, and specialized data annotation companies like Appen and Scale Labs vying for market share. Geographic distribution shows a strong concentration in North America and Europe initially, but Asia-Pacific is expected to emerge as a major growth region in the coming years, driven primarily by China and India's expanding technology sectors. The market, however, faces certain challenges. The high cost of data annotation, particularly for complex tasks such as video annotation, can pose a barrier to entry for smaller companies. Ensuring data quality and accuracy remains a significant concern, requiring robust quality control mechanisms. Furthermore, ethical considerations surrounding data privacy and bias in algorithms require careful attention. To overcome these challenges, companies are investing in automation tools and techniques like synthetic data generation, alongside developing more sophisticated quality control measures. The future of the Data Annotation and Collection Services market will likely be shaped by advancements in AI and ML technologies, the increasing availability of diverse data sets, and the growing awareness of ethical considerations surrounding data usage.

1. Overview This dataset is a collection of high view traffic images in multiple scenes, backgrounds and lighting conditions that are ready to use for optimizing the accuracy of computer vision models. All of the contents is sourced from PIXTA's stock library of 100M+ Asian-featured images and videos. PIXTA is the largest platform of visual materials in the Asia Pacific region offering fully-managed services, high quality contents and data, and powerful tools for businesses & organisations to enable their creative and machine learning projects.

2. Use case This dataset is used for AI solutions training & testing in various cases: Traffic monitoring, Traffic camera system, Vehicle flow estimation,... Each data set is supported by both AI and human review process to ensure labelling consistency and accuracy. Contact us for more custom datasets.

3. About PIXTA PIXTASTOCK is the largest Asian-featured stock platform providing data, contents, tools and services since 2005. PIXTA experiences 15 years of integrating advanced AI technology in managing, curating, processing over 100M visual materials and serving global leading brands for their creative and data demands. Visit us at https://www.pixta.ai/ for more details.

The global data labeling tools market is projected to reach a value of USD 12.19 billion by 2033, expanding at a CAGR of 31.9% during the forecast period of 2025-2033. The growing volume of unstructured data, the increasing adoption of AI and ML technologies, and the need for high-quality labeled data for training machine learning models are the key factors driving market growth. The market is segmented by type into cloud-based and on-premises solutions, with the cloud-based segment holding a dominant share due to its scalability, cost-effectiveness, and flexibility. By application, the market is divided into IT, automotive, government, healthcare, financial services, retail, and others. The IT segment is expected to account for the largest share during the forecast period as businesses increasingly adopt AI and ML technologies to automate their processes and gain insights from data.

LandCoverNet is a global annual land cover classification training dataset with labels for the multi-spectral satellite imagery from Sentinel-1, Sentinel-2 and Landsat-8 missions in 2018. LandCoverNet Asia contains data across Asia, which accounts for ~31% of the global dataset. Each pixel is identified as one of the seven land cover classes based on its annual time series. These classes are water, natural bare ground, artificial bare ground, woody vegetation, cultivated vegetation, (semi) natural vegetation, and permanent snow/ice.
There are a total of 2753 image chips of 256 x 256 pixels in LandCoverNet South America V1.0 spanning 92 tiles. Each image chip contains temporal observations from the following satellite products with an annual class label, all stored in raster format (GeoTIFF files):
* Sentinel-1 ground range distance (GRD) with radiometric calibration and orthorectification at 10m spatial resolution
* Sentinel-2 surface reflectance product (L2A) at 10m spatial resolution
* Landsat-8 surface reflectance product from Collection 2 Level-2

Radiant Earth Foundation designed and generated this dataset with a grant from Schmidt Futures with additional support from NASA ACCESS, Microsoft AI for Earth and in kind technology support from Sinergise.

The global Artificial Intelligence (AI) Training Dataset market is projected to reach $1605.2 million by 2033, exhibiting a CAGR of 9.4% from 2025 to 2033. The surge in demand for AI training datasets is driven by the increasing adoption of AI and machine learning technologies in various industries such as healthcare, financial services, and manufacturing. Moreover, the growing need for reliable and high-quality data for training AI models is further fueling the market growth. Key market trends include the increasing adoption of cloud-based AI training datasets, the emergence of synthetic data generation, and the growing focus on data privacy and security. The market is segmented by type (image classification dataset, voice recognition dataset, natural language processing dataset, object detection dataset, and others) and application (smart campus, smart medical, autopilot, smart home, and others). North America is the largest regional market, followed by Europe and Asia Pacific. Key companies operating in the market include Appen, Speechocean, TELUS International, Summa Linguae Technologies, and Scale AI. Artificial Intelligence (AI) training datasets are critical for developing and deploying AI models. These datasets provide the data that AI models need to learn, and the quality of the data directly impacts the performance of the model. The AI training dataset market landscape is complex, with many different providers offering datasets for a variety of applications. The market is also rapidly evolving, as new technologies and techniques are developed for collecting, labeling, and managing AI training data.

LandCoverNet is a global annual land cover classification training dataset with labels for the multi-spectral satellite imagery from Sentinel-1, Sentinel-2 and Landsat-8 missions in 2018. LandCoverNet North America contains data across North America, which accounts for ~13% of the global dataset. Each pixel is identified as one of the seven land cover classes based on its annual time series. These classes are water, natural bare ground, artificial bare ground, woody vegetation, cultivated vegetation, (semi) natural vegetation, and permanent snow/ice.

There are a total of 1561 image chips of 256 x 256 pixels in LandCoverNet North America V1.0 spanning 40 tiles. Each image chip contains temporal observations from the following satellite products with an annual class label, all stored in raster format (GeoTIFF files):
* Sentinel-1 ground range distance (GRD) with radiometric calibration and orthorectification at 10m spatial resolution
* Sentinel-2 surface reflectance product (L2A) at 10m spatial resolution
* Landsat-8 surface reflectance product from Collection 2 Level-2

Radiant Earth Foundation designed and generated this dataset with a grant from Schmidt Futures with additional support from NASA ACCESS, Microsoft AI for Earth and in kind technology support from Sinergise.

The dataset building service market is projected to grow significantly in the coming years, driven by the increasing demand for data-driven insights and the growth of artificial intelligence (AI) and machine learning (ML) technologies. The global dataset building service market size was valued at USD XXX million in 2025 and is expected to expand at a compound annual growth rate (CAGR) of XX% from 2025 to 2033. This growth can be attributed to the increasing adoption of AI and ML technologies, which require large and diverse datasets for training and testing. Additionally, the rising demand for data-driven insights for decision-making is driving the growth of the dataset building services market. Key market trends include the growing popularity of cloud-based dataset building services, the increasing adoption of data annotation and labeling services, and the emergence of new data sources such as social media and IoT devices. The major players in the dataset building service market include Appen, Scale AI, Lionbridge, Samasource, CloudFactory, Deepen AI, and Clarifai. These companies offer a wide range of dataset building services, including data collection, annotation, and labeling. The market is expected to witness further consolidation in the coming years, as larger players acquire smaller companies to expand their service offerings and geographic reach.

LandCoverNet is a global annual land cover classification training dataset with labels for the multi-spectral satellite imagery from Sentinel-1, Sentinel-2 and Landsat-8 missions in 2018. LandCoverNet Australia contains data across Australia, which accounts for ~7% of the global dataset. Each pixel is identified as one of the seven land cover classes based on its annual time series. These classes are water, natural bare ground, artificial bare ground, woody vegetation, cultivated vegetation, (semi) natural vegetation, and permanent snow/ice.
There are a total of 600 image chips of 256 x 256 pixels in LandCoverNet Australia V1.0 spanning 20 tiles. Each image chip contains temporal observations from the following satellite products with an annual class label, all stored in raster format (GeoTIFF files):
* Sentinel-1 ground range distance (GRD) with radiometric calibration and orthorectification at 10m spatial resolution
* Sentinel-2 surface reflectance product (L2A) at 10m spatial resolution
* Landsat-8 surface reflectance product from Collection 2 Level-2

Radiant Earth Foundation designed and generated this dataset with a grant from Schmidt Futures with additional support from NASA ACCESS, Microsoft AI for Earth and in kind technology support from Sinergise.

About This Dataset

The Roboflow Website Screenshots dataset is a synthetically generated dataset composed of screenshots from over 1000 of the world's top websites. They have been automatically annotated to label the following classes: :fa-spacer: * button - navigation links, tabs, etc. * heading - text that was enclosed in <h1> to <h6> tags. * link - inline, textual <a> tags. * label - text labeling form fields. * text - all other text. * image - <img>, <svg>, or <video> tags, and icons. * iframe - ads and 3rd party content.

Example

This is an example image and annotation from the dataset: https://i.imgur.com/mOG3u3Z.png" alt="WIkipedia Screenshot">

Usage

Annotated screenshots are very useful in Robotic Process Automation. But they can be expensive to label. This dataset would cost over $4000 for humans to label on popular labeling services. We hope this dataset provides a good starting point for your project. Try it with a model from our model library.

Collecting Custom Data

Roboflow is happy to provide a custom screenshots dataset to meet your particular needs. We can crawl public or internal web applications. Just reach out and we'll be happy to provide a quote!

About Roboflow

Roboflow makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless. :fa-spacer: Developers reduce 50% of their boilerplate code when using Roboflow's workflow, save training time, and increase model reproducibility. :fa-spacer:

Bitext - Retail Banking Tagged Training Dataset for LLM-based Virtual Assistants

  Overview

This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [Retail Banking] sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-retail-banking-llm-chatbot-training-dataset.

Context and Aim

Deep learning in Earth Observation requires large image archives with highly reliable labels for model training and testing. However, a preferable quality standard for forest applications in Europe has not yet been determined. The TreeSatAI consortium investigated numerous sources for annotated datasets as an alternative to manually labeled training datasets.

We found the federal forest inventory of Lower Saxony, Germany represents an unseen treasure of annotated samples for training data generation. The respective 20-cm Color-infrared (CIR) imagery, which is used for forestry management through visual interpretation, constitutes an excellent baseline for deep learning tasks such as image segmentation and classification.

Description

The data archive is highly suitable for benchmarking as it represents the real-world data situation of many German forest management services. One the one hand, it has a high number of samples which are supported by the high-resolution aerial imagery. On the other hand, this data archive presents challenges, including class label imbalances between the different forest stand types.

The TreeSatAI Benchmark Archive contains:

• 50,381 image triplets (aerial, Sentinel-1, Sentinel-2)

• synchronized time steps and locations

• all original spectral bands/polarizations from the sensors

• 20 species classes (single labels)

• 12 age classes (single labels)

• 15 genus classes (multi labels)

• 60 m and 200 m patches

• fixed split for train (90%) and test (10%) data

• additional single labels such as English species name, genus, forest stand type, foliage type, land cover

The geoTIFF and GeoJSON files are readable in any GIS software, such as QGIS. For further information, we refer to the PDF document in the archive and publications in the reference section.

Version history

v1.0.0 - First release

Citation

Ahlswede et al. (in prep.)

GitHub

Full code examples and pre-trained models from the dataset article (Ahlswede et al. 2022) using the TreeSatAI Benchmark Archive are published on the GitHub repositories of the Remote Sensing Image Analysis (RSiM) Group (https://git.tu-berlin.de/rsim/treesat_benchmark). Code examples for the sampling strategy can be made available by Christian Schulz via email request.

Folder structure

We refer to the proposed folder structure in the PDF file.

• Folder “aerial” contains the aerial imagery patches derived from summertime orthophotos of the years 2011 to 2020. Patches are available in 60 x 60 m (304 x 304 pixels). Band order is near-infrared, red, green, and blue. Spatial resolution is 20 cm.

• Folder “s1” contains the Sentinel-1 imagery patches derived from summertime mosaics of the years 2015 to 2020. Patches are available in 60 x 60 m (6 x 6 pixels) and 200 x 200 m (20 x 20 pixels). Band order is VV, VH, and VV/VH ratio. Spatial resolution is 10 m.

• Folder “s2” contains the Sentinel-2 imagery patches derived from summertime mosaics of the years 2015 to 2020. Patches are available in 60 x 60 m (6 x 6 pixels) and 200 x 200 m (20 x 20 pixels). Band order is B02, B03, B04, B08, B05, B06, B07, B8A, B11, B12, B01, and B09. Spatial resolution is 10 m.

• The folder “labels” contains a JSON string which was used for multi-labeling of the training patches. Code example of an image sample with respective proportions of 94% for Abies and 6% for Larix is: "Abies_alba_3_834_WEFL_NLF.tif": [["Abies", 0.93771], ["Larix", 0.06229]]

• The two files “test_filesnames.lst” and “train_filenames.lst” define the filenames used for train (90%) and test (10%) split. We refer to this fixed split for better reproducibility and comparability.

• The folder “geojson” contains geoJSON files with all the samples chosen for the derivation of training patch generation (point, 60 m bounding box, 200 m bounding box).

CAUTION: As we could not upload the aerial patches as a single zip file on Zenodo, you need to download the 20 single species files (aerial_60m_…zip) separately. Then, unzip them into a folder named “aerial” with a subfolder named “60m”. This structure is recommended for better reproducibility and comparability to the experimental results of Ahlswede et al. (2022),

Join the archive

Model training, benchmarking, algorithm development… many applications are possible! Feel free to add samples from other regions in Europe or even worldwide. Additional remote sensing data from Lidar, UAVs or aerial imagery from different time steps are very welcome. This helps the research community in development of better deep learning and machine learning models for forest applications. You might have questions or want to share code/results/publications using that archive? Feel free to contact the authors.

Project description

This work was part of the project TreeSatAI (Artificial Intelligence with Satellite data and Multi-Source Geodata for Monitoring of Trees at Infrastructures, Nature Conservation Sites and Forests). Its overall aim is the development of AI methods for the monitoring of forests and woody features on a local, regional and global scale. Based on freely available geodata from different sources (e.g., remote sensing, administration maps, and social media), prototypes will be developed for the deep learning-based extraction and classification of tree- and tree stand features. These prototypes deal with real cases from the monitoring of managed forests, nature conservation and infrastructures. The development of the resulting services by three enterprises (liveEO, Vision Impulse and LUP Potsdam) will be supported by three research institutes (German Research Center for Artificial Intelligence, TU Remote Sensing Image Analysis Group, TUB Geoinformation in Environmental Planning Lab).

Publications

Ahlswede et al. (2022, in prep.): TreeSatAI Dataset Publication

Ahlswede S., Nimisha, T.M., and Demir, B. (2022, in revision): Embedded Self-Enhancement Maps for Weakly Supervised Tree Species Mapping in Remote Sensing Images. IEEE Trans Geosci Remote Sens

Schulz et al. (2022, in prep.): Phenoprofiling

Conference contributions

S. Ahlswede, N. T. Madam, C. Schulz, B. Kleinschmit and B. Demіr, "Weakly Supervised Semantic Segmentation of Remote Sensing Images for Tree Species Classification Based on Explanation Methods", IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 2022.

C. Schulz, M. Förster, S. Vulova, T. Gränzig and B. Kleinschmit, “Exploring the temporal fingerprints of mid-European forest types from Sentinel-1 RVI and Sentinel-2 NDVI time series”, IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 2022.

C. Schulz, M. Förster, S. Vulova and B. Kleinschmit, “The temporal fingerprints of common European forest types from SAR and optical remote sensing data”, AGU Fall Meeting, New Orleans, USA, 2021.

B. Kleinschmit, M. Förster, C. Schulz, F. Arias, B. Demir, S. Ahlswede, A. K. Aksoy, T. Ha Minh, J. Hees, C. Gava, P. Helber, B. Bischke, P. Habelitz, A. Frick, R. Klinke, S. Gey, D. Seidel, S. Przywarra, R. Zondag and B. Odermatt, “Artificial Intelligence with Satellite data and Multi-Source Geodata for Monitoring of Trees and Forests”, Living Planet Symposium, Bonn, Germany, 2022.

C. Schulz, M. Förster, S. Vulova, T. Gränzig and B. Kleinschmit, (2022, submitted): “Exploring the temporal fingerprints of sixteen mid-European forest types from Sentinel-1 and Sentinel-2 time series”, ForestSAT, Berlin, Germany, 2022.