We formally define the aerial scanning problem. A set of unmanned aerial vehicles (UAVs) cooperate to frequently scan a given geographic area. Each UAV can only photograph a small portion of it at a time. Each UAV maintains the necessary information to create a global picture of the geographic area. This global picture consists of smaller pictures that are taken within small time of each other ...

We present a damage pattern mining framework for hurricane data on residential houses using aerial photographs with 1:3000 based scale. The vertical photographs are normally collected for an overview of a disaster area or for more detailed assessment. Damage on roof, especially for shingles torn off, is expected to be discovered in a more efficient and automatic way instead of going through the...

The country between Arthur'~ Lakes and the Lake HiveI' shown on Plate IV is drained in the western part by Tumbledown Creek and .Tones Rivulet which flow into the Eastern Lake, and in the eastern by the tributaries of the Lab' River. The escarpment of the Western Tiers marks the eastem margin of the Central Plateau of Tasmania. Mapping was carried out with the assistance of aerial photographs a...

This paper describes a new second-generation-type lossy image compression scheme using wavelets for aerial ortho images. For map applications where some losses are allowed, aerial ortho images present a possibility of having a very high compression ratio, because they are natural images with many uniform (coherent) subregions. However classical compression schemes such as the joint-photograph e...

We review some pattern matching algorithms and techniques motivated by the discrete theory of image processing. The problem inspiring this research is that of searching an aerial photograph for all appearances of some object. The issues we discuss are digitization, local errors, rotation and scaling. We review deterministic serial techniques that are used for multidimensional pattern matching a...

We review some recent algorithms motivated by computer vision. The problem inspiring this research is that of searching an aerial photograph for all appearances of some object. The issues we discuss are local errors, scaling, compression and dictionary matching. We review deterministic serial techniques that are used for multidimensional pattern matching and discuss their strengths and weaknesses.