Object-based image classification for land-cover mapping purposes using remote-sensing
imagery has attracted significant attention in recent years. Numerous studies conducted over …

Landslides represent major natural hazards, which cause every year significant loss of lives
and damages to buildings, properties and lifelines. In the last decades, a significant increase …

随着全球气候变化, 矿产资源开采和大型人类工程活动的不断加剧, 冰崩, 塌陷, 滑坡,
地面沉降和地裂缝等多类型地质灾害呈现高频性和链生性的趋势, 灾害后果更加严重 …

Random Forest (RF) is a widely used algorithm for classification of remotely sensed data.
Through a case study in peatland classification using LiDAR derivatives, we present an …

Landslide mapping is the primary step for landslide investigation and prevention. At present,
both the accuracy and the degree of automation of landslide mapping with remote sensing …

Land subsidence (LS) is among the most critical environmental problems, affecting both
agricultural sustainability and urban infrastructure. Existing methods often use either simple …

Since vegetation in urban areas delivers crucial ecological services as a support to human
well-being and to the urban population in general, its monitoring is a major issue for urban …

Slow-moving landslides are widespread in many landscapes with significant impacts on the
topographic relief, sediment transfer and human settlements. Their area-wide mapping and …

Support vector machine (SVM) is one of the most successful classifiers for remote sensing
image classification. However, the performance of SVM is mainly dependent on its …

How to acquire landslide disaster information quickly and accurately has become the focus
and difficulty of disaster prevention and relief by remote sensing. Landslide disasters are …