Abstract: Disclosed are implementations that include a method for detecting damage in a geographical area, including receiving a first image of the geographical area, captured after occurrence of a damage-causing event in the geographical area, and obtaining a second image of the geographical area, the second image including image data of the geographical area prior to the occurrence of the damage-causing event, with the first and second images containing an overlapping portion comprising one or more common objects. The method also includes obtaining markers for the first and second images, with the markers being geometrical shapes corresponding to objects in the first and second images, and determining damage suffered by an object, from the one or more common objects, based on differences between a first geometrical shape corresponding to the object appearing in the first image and a second geometrical shape corresponding to the object appearing in the second image.

Abstract: Disclosed are devices, systems, apparatus, methods, products, and other implementations, including a method for detecting damage in a geographical area that includes receiving image data for the geographical area, the image data containing data representative of one or more objects, obtaining damage-index data comprising information indicating potential damage affecting the geographical area, and detecting damage to an object, from the one or more objects in the geographical area, based on the received image data for the geographical area and the damage-index data comprising the information indicating the potential damage affecting the geographical area.

Abstract: Disclosed are devices, systems, apparatus, methods, products, and other implementations, including a method for detecting damage in a geographical area that includes receiving a first image of the geographical area, with the first image having a first resolution, and detecting damage to at least one object appearing in the first image in response to applying a trained learning engine to the first image. The learning engine is trained to detect damage in geographical areas based on received one or more images in a second set of images having a second resolution higher than the first resolution, and based on one or more images in a first set of images having the first resolution. The one or more images in the first set of images and the one or more images in the second set of images include one or more overlapping portions in which one or more objects appear.