Build change detection models that reveal land transformations with accuracy and speed.
Navigate the landscape of change detection with Picterra Forge. Our platform offers a robust toolkit for rapid development of specialized change detection models, streamlined annotations and efficient algorithm training, as well as meticulous change analysis. Uncover shifts in land patterns and objects, from detecting subtle changes to exploring comprehensive transformations across terrains.
Focus on when objects or textures vanish in imagery. Examples: individual trees being cut, forest patches cleared, buildings demolished, or water bodies shrinking.
The opposite of disappearing — new features show up between two dates. Examples: trees being planted, buildings under construction, oil spills, or piles of construction material.
Detect objects that move — often in infrastructure. Examples: railway connectors shifting due to wear, displaced equipment, or other elements that signal potential risks.
Not about appearing or vanishing, but transformation in how something looks. Examples: a building roof being added, or NDVI shifts in crops that indicate changes in health and vitality.
Simultaneusly annotate targeted changes across automatically paired images.
Train the model with combined image pair and change annotations.
Run trained model on the image(s) where you want to detect changes.
Picterra’s change detection model is trained directly on pairs of “before” and “after” images. Instead of labeling objects in a single image, you annotate the changes across both views simultaneously with the help of the Split View tool. This feature displays the two images side by side and mirrors your annotations across both, making it easier and faster to capture the change itself.
The approach lets you highlight only the transitions that matter—such as a building under construction, trees being cleared, or new infrastructure appearing—while filtering out irrelevant differences. The model then applies these examples to automatically detect similar patterns of change at scale.
Picterra’s change detection model is trained directly on pairs of “before” and “after” images. Instead of labeling objects in a single image, you annotate the changes across both views simultaneously with the help of the Split View tool. This feature displays the two images side by side and mirrors your annotations across both, making it easier and faster to capture the change itself.
The approach lets you highlight only the transitions that matter—such as a building under construction, trees being cleared, or new infrastructure appearing—while filtering out irrelevant differences. The model then applies these examples to automatically detect similar patterns of change at scale.
Detect a wide range of changes — from objects appearing or disappearing to shifts in condition or movement.
Focus your annotations on exactly the type of change you care about.
Faster analysis – one model processes both images together instead of running separate detections.
Flexible and scalable across multiple geographies and use cases.
