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Development of change detection methods that are functional and reliable enough for operational work is still a demanding task. Thisarticle discusses automated change detection from the viewpoint of one case study: the Finnish Land Parcel Identification System(FLPIS). The objective of the study is to develop a change detection method that could be used as an aid in the updating of theFLPIS. The method is based on object-based interpretation, and it uses existing parcel boundaries and new aerial ortho images asinput data. Rules for classifying <i>field</i> and <i>non-field</i> objects are defined automatically by using the classification tree method andtraining data. Additional, manually created rules are used to improve the results. Classification tests carried out during thedevelopment work suggest that real changes can be detected relatively well. According to a recent visual evaluation, 96% of changeslarger than 100 m2 were detected, at least partly. The overall accuracy of the change detection results was 93% when compared withreference data pixel-by-pixel. On the other hand, there are also missing changes and numerous false alarms. The main challengesencountered in the method development include the wide diversity of agricultural fields and other land cover objects locally, acrossthe country, and at different times of the spring and summer, variability in the digital numbers (DNs) of the aerial images, thedifferent nature of visual and automatic interpretation, and the small percentage of the total field area that has really changed. Thesechallenges and possible solutions are discussed in the article

THE CHALLENGE OF AUTOMATED CHANGE DETECTION: DEVELOPING A METHOD FOR THE UPDATING OF LAND PARCELS