Machine learning from remote sensing analysis

An intelligent system (SEIDAM-System of Experts for Intelligent Data Management) is being developed for answering queries about the forests and the environment through the integration of remote sensing, geographic information, models and field measurements. SEIDAM consists of an hierarchical group of expert systems. Machine learning and planning can be used to create plans that execute image analysis software in order to recognize specific objects and perform a variety of different tasks. A query (task) could require, for example, that forest inventory stored in a GIS be updated to reflect past harvesting. As sensors become more numerous, the choices of data and options to recognize objects become more complex. These complexities can be reduced by making use of case-based reasoning. Only the data needed to answer the query will be used. The aim of case-based reasoning is to avoid having to build a solution to a problem from first principles, or by drawing on rare expertise, by adapting a known solution for an old problem to the new problem. There is a constantly growing variety of data sets. Providing information at various degrees of accuracy and at often different cost. A non-expert user of this data could greatly benefit from reusing specific cases of queries, which convert the data into knowledge that other users were seeking before. A case, in this context, consists of a query and an example of the process (plan) that answers that query using a single or a multi-sensor data set, and geographic information, such as forest cover, topography, hydrology, etc. In their earlier work, the authors constructed a planner (LEAR), a planning system for creating expert systems by executing software for a case and interacting with a human expert. they now wish to raise the machine learning methods from creating expert systems for executing existing software to creating new rules (knowledge) derived from observing remote sensing analysis cases. Knowledge about objects acquired by the LEAR planner can be used to assist a case-based reasoner during both its retrieval step and its adaptation step