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« Ingedata does a really good job. It’s great how we set a labeling process to segment and label our satellite images. They built a specialized team of annotators for photo interpretation and my data labeling pipeline is now more efficient and qualitative. It’s almost like having my own team of annotators. I just choose the image dataset and add it to Ingedata’s annotation backlog. Thanks, guys! »

Renaud Allioux CTO @Preligens

Space is the final frontier for AI. Satellite data doubles each year, but in terms of capacity we have barely scratched the surface

Emerging technologies such as Computer Vision and automated image analysis have improved satellite data interpretation. AI can now be used to provide accurate, timely, and relevant information to decision makers on a global scale.

The incredible amounts of data generated by sources such as satellites and Earth observation are a key area where AI can offer enormous opportunities, as it provides a wealth of information for discoveries, innovations, and services that improve society.

Our offer

POLYGONS

BOUNDING BOXES

LANDMARK POSITIONING

Polygons

Polygons are a very popular segmentation technique that accurately outlines a specific area of interest and exclude elements of background that would lower the performance of your Computer Vision model. While not as accurate as Semantic Segmentation, they are much faster to draw, making them a strong option to train your model with both accurate and high-volume annotations.

polygon

Bounding Boxes

Bounding Boxes is one of the most common segmentation techniques, as it outlines the shape of an object by defining its X and Y coordinates. They are fast to draw but might include some background that will lower the accuracy with which your model is detected.

fashion-annot

Landmark Positioning

Small objects are identified and counted using keypoints. This technique is efficient for small objects that appear in large quantity in the data.

landmark

POLYGONS

Polygons

Polygons are a very popular segmentation technique that accurately outlines a specific area of interest and exclude elements of background that would lower the performance of your Computer Vision model. While not as accurate as Semantic Segmentation, they are much faster to draw, making them a strong option to train your model with both accurate and high-volume annotations.

BOUNDING BOXES

Bounding Boxes

Bounding Boxes is one of the most common segmentation techniques, as it outlines the shape of an object by defining its X and Y coordinates. They are fast to draw but might include some background that will lower the accuracy with which your model is detected.

LANDMARK POSITIONING

Landmark Positioning

Small objects are identified and counted using keypoints. This technique is efficient for small objects that appear in large quantity in the data.

They trust us

We offer high-end services with our unique data production methodology and, more importantly, our wonderful team of 500. As a people company, Ingedata thrives on bringing talents from developing countries on the international AI scene.

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Our expertise

Ingedata’s key differentiator is our ability to extract highly accurate information

Ingedata has the largest photo interpretation team in Africa, with over 100 photo interpreters working on Earth observation projects.

We even work for the French and the US Departments of Defense, which demonstrates our ability to fit into projects with high confidentiality requirements.

By offering a high-precision geospatial imagery service, our team of experts provides accurate information to our clients, which gives them a recognized competitive advantage.

Client stories

Our Case Studies

Learn more about our approach and explore our case studies

The path to success

Why trust us?

With more than 100 projects in the field of ML model training, we are recognized for our remarkable know-how in production management and our guarantee of quality.

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Confidentiality

Externalizing your data can represent a significant risk in terms of loss, deterioration, or theft. At Ingedata, your projects are designed and carried out in-house, from our secure production centers.

Dedicated teams

Ingedata's annotators have degrees ranging from bachelor's to engineer's or doctorate in your field. All our teams work from our production centers and are trained in the specific requirements of preparing data for machine learning.

Specific Datasets

Accelerate the optimization of your algorithm by using data prepared specifically for you. We collect, enrich, and categorize your data to build your own datasets.

Autonomous management

We fit seamlessly into your current production mode, take over any coordination and adapt our team to your specific constraints in terms of data volume and quality.