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Geospatial Annotation Tool

Build a high-quality geospatial training dataset in minutes on Kili Technology.
Label GeoTIFF files effortlessly with various annotation tasks, including bounding boxes, polygons, lines, segmentation, object detection - augmentable with your own machine learning model. Go faster with our user interface featuring intuitive zoom, tiling, and image editing features. Boost productivity by labeling and leveraging metadata in a single interface. Autosave your work for peace of mind.

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Small business (50 or fewer employees)

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Small business (50 or fewer employees)

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Maximize Labeling Efficiency for Satellite Imagery

Labeling geospatial images is complex. With Kili Technology’s image annotation tool, unlock the unparalleled ability to deliver fast & accurate geospatial training data. Simply upload your files in their native GeoTIFF formats and label them directly within our user interface.

GeoTIFF native support

Large file support & tiling

In-app image edition

Metadata native management

Geo-coordinates extraction



Achieve Top Data Quality for Geospatial Annotations

Deliver datasets fast but also of the highest quality with Kili Technology. Streamline your quality review & fix issues in-app with our Explore view. Collaborate seamlessly with teammates using labeler, reviewer, and admin permission levels. Minimize human errors with trained models and use advanced quality metrics to quantify your training data's quality. Automate programmatic QA with our plugins and workflows for a seamless labeling process.

Explore view for quality

User permission levels

Advanced quality metrics

Seamlessly Manage DataOps for Aerial Imagery Annotation

Implement your labeling in your ML stack seamlessly. Store your training data on Kili Technology's platform or connect to your remote storage on Azure, Google, or AWS. Our flexible integration options enable you to integrate Kili Technology effortlessly into your existing ML stack, streamlining your entire machine learning workflow. With Kili Technology, you have everything you need to optimize your labeling processes and build top-quality datasets.

Single Sign On (SSO)

Remote storage

API & Python SDK


They Trust Us

"Kili's customer support is best in-class. We solve issues much faster and it has a direct impact on our performance."

Andrea Colonna
Andrea ColonnaHead of Data, Jellysmack

"Great companies like Kili Technology, [...] have already adopted this data-centric AI approach."

Andrew Ng
Andrew NgData-centric AI Influencer

"Kili is bringing added value in the management of our projects and this is quality."

Gilles Henaff
Gilles HenaffHead of AI, Thales Las France

"Kili enables us to improve our models’ performance and scale our AI projects as fast as our business needs."

Andrea Colonna
Andrea ColonnaHead of Data, Jellysmack

"We are very satisfied with our collaboration with Kili. We saw a performance improvement of our model of 3.5%"

Marie de Léséleuc
Marie de Léséleuc Director of Analytics and Data Science, Eidos-Montréal

Frequent Questions

What is geospatial data & geospatial annotation?

Geospatial data, also called geospatial imagery, satellite imagery or aerial imagery, are images taken from satellites. They are usually in specific data types: GeoTIFF as well as .acs and .img. Geospatial annotation is the action of labeling geospatial data to train machine learning models.

How can you use geospatial data to train machine learning models?
Satellite imagery is essential to train Machine Learning models or computer vision models that aim to analyze our world: fire and natural disasters prevention, deforestation monitoring, traffic and weather analysis. By analyzing and labeling geospatial images, you can build any AI app based on aerial imagery by creating a powerful training dataset.
What are the labeling tasks you can do on satellite imagery?
With Kili Technology, our image annotation tools support GeoTiff files, meaning you can do all labeling tasks on aerial imagery: object detection, image segmentation, box annotations, track objects, image classification. And you can do them with a selection of tools: bounding boxes, polygons, semantic & interactive segmentation, and much more.
What are the labeling formats supported by Kili Technology?
What are the labeling formats supported by Kili Technology? Kili Technology, as a training data labeling platform, supports labeling tasks on all asset types. Computer vision tasks: image classification, video classification, bounding box, polygon, point, line, geospatial data annotation, object tracking, object detection, etc. Natural language processing with text annotation (rich text, and conversation). Document annotation (documents, pdfs, OCR). On text and documents, you can do classification, named entity recognition, and objects relations to name a few.
How is Kili Technology different from other image and video annotation and tools?

Kili Technology platform is different because we put quality at the core of our product. Many low-cost labeling tools focus on improving labeling productivity, which we do as well, but disregard the focus on creating quality training data. For instance, you can inspect the data distributions of your annotation and detect mistakes. Kili Technology is a training data platform where the annotation process is dedicated to data quality.

How does Kili Technology ensure data security in my annotation process?
Kili Technology as an image annotation tool is fully secure with a SOC2, ISO 27001 & HIPAA certification. We put a high priority on privacy preserving images, as well as any kind of protection against bias (perceived gender presentation, cultural and racial representation, etc). We also provide different deployment options to fit the data security needs of our customers. Note that data management options may vary depending on your hosting mode (Cloud or On-premise).
Is Kili Technology providing automatic annotation?
Kili Technology's API is accessible to our users. Therefore, you can connect your machine-learning model to generate pre-annotations. We also support segmentation tasks augmented with the Segment Anything Model (SAM) and any other foundation model with prompt engineering or segmentation model based on input prompts. To learn more about prompt engineering, you can check our recent webinars here.
Is Kili Technology an open-source software?
Kili Technology is not an open-source software. However, you can use our free plan to do image annotation and computer vision tasks, use our geospatial tools & segmentation masks, and do everything needed to output multiple valid masks & training data into powerful datasets and in the end, powerful segmentation models. Note that when using our free plan, you may not benefit from all our various tools at 100% of their capacity.
What are concepts related to labeling aerial imagery that I need to know?
There are a few things that ML engineers should understand when working with geospatial imagery and data labeling. Here is a list of definitions to help you. remote sensing: Remote sensing is the process of detecting and monitoring the physical characteristics of an area by measuring its reflected and emitted radiation at a distance deep learning: Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. cloud optimized geotiffs: A Cloud Optimized GeoTIFF (COG) is a regular GeoTIFF file, aimed at being hosted on a HTTP file server, with an internal organization that enables more efficient workflows on the cloud. geospatial use cases: many machine learning & deep learning use cases leverage aerial imagery & computer vision. The main examples are the analysis of land use, prevention of natural disasters, defense and military surveillance, weather prediction, and many more. These use cases are time consuming to build as they require a cloud or on-prem labeling tool, satellite images to label, a team capable of annotating & analyzing objects on the image to create labels, and more. Machine learning models built with geospatial data and the right annotation tools are extremely powerful.
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