Vzdrževanje podatkov v topografskih bazah je ena od pomembnejših nalog organizacij, ki te podatkovne baze vodijo. Eden od pomembnih podatkovnih slojev v topografskih bazah so podatki o stavbah. V člankusta opisani metoda za samodejen zajem stavb iz digitalnega modela površja in multispektralnega ortofota ter uporaba rezultatov zajema za samodejno iskanje sprememb v topografskih bazah, v katerih se vodijo podatki o stavbah. Začetno masko stavb smo izdelali iz normaliziranega digitalnega modela površja (nDMP). Vegetacijo smo iz maske stavb izločili z modificiranim vegetacijskim indeksom, izračunanim iz infrardečega ortofota ob upoštevanju indeksa senc in teksture nDMP. Na končni maski smo stavbe vektorizirali z uporabo transformacije Radon. Rezultate samodejnega zajema stavb smo primerjali s katastrom stavb in dejanskim stanjem na terenu. Ssamodejnim postopkom smo našli 94,4 % vseh stavb na območju in ocenili, da je opisana metoda primerna za zajem podatkov o stavbah za topografske baze v merilih 1 : 10 000 in manj. Rezultat samodejnegaiskanja sprememb (popolnost 93,5 % in pravilnost 78,4 %) kaže, da je opisana metoda primerna za iskanje sprememb med podatki o stavbah ; The update of topographic databases is an important task for organizations that maintain them. Building data are one of the important data types in topographic databases. The article describes a method for automatic building extraction from digital surfacemodel and multispectral orthophoto and the use of extraction results for the building change detection in the topographic database. The initial building mask was created from the normalized digital surface model (nDSM).Vegetation was eliminated from the building mask using a modified vegetation index calculated from the infrared orthophoto and also considering the shadow index and the nDMP texture. The finalbuilding mask was vectorised using Radon transform. The results of the automatic building extraction were compared to the building cadastre and the actual situation on the ground. The automatic methoddetected 94.4% of all buildings in the area. We concluded that the described method is appropriate for capturing of the building data for the topographic database in scales 1 : 10 000 and smaller. Automatic change detection results (completeness 93.5% and correctness 78.4%) indicate that the described method is appropriate for building change detection.