This paper presents a methodology for quantifying the number of buildings that collapsed following the Bam earthquake. The approach is object rather than pixel-oriented, commencing with the inventory of buildings as objects in high-resolution QuickBird satellite imagery captured before the event. The number of collapsed structures is computed based on the unique statistical characteristics of these objects/buildings within the “after” scene. A total of 18,872 structures were identified within Bam, of which the results suggest that 34% collapsed—a total of 6,473. Preliminary assessments indicate an overall accuracy for the damage classification of 70.5%.

No abstract

Temporal pattern of the altitudinal limit of beech forest in the Northern Apennines. A photogrammetric analysis. The temporal pattern of the High Reggio Emilia Apennine upper timberline during the second half of the 19 th century has been studied at landscape level in the Prado-Cusna area since 1954. This part of the Northern Apennines is of outstanding biological value. Orthorectified aerial photographs (flights GAI 1954, RER 1978, IT 2000 were used to generate maps using an object-based method of classification. In the period studied the timberline was relatively stable despite the decline of local historical human impact based on agriculture and pastoralism on high mountain vegetation and the mean temperature in the northern Apennines has increased by about 1.3°C. Problems linked to the photogrammetric data processing of aerial photographs of the past and to the classification of images are briefly discussed.

O presente trabalho descreve um estudo de um protótipo de um veículo autônomo para detecção e combate a incêndio em áreas de risco. Cabe ao veículo seguir a linha utilizando sensores infravermelhos, detectar o fogo utilizando sensores de chama e apaga-lo utilizando bombas de água, além de que, se porventura houver algum obstáculo o mesmo deve perceber utilizando um sensor de distância, parar e executar uma ação, tudo de forma autônoma.