Due to the high data demand of machine learning algorithms, multiple datasets are emerging in remote sensing. But these datasets are costly and time consuming to annotate especially for change detection or natural phenomena monitoring. In particular, early warning systems on slow-moving disasters are lacking of training datasets as they require both geomorphological and SAR interferometry expertise. In this paper, (i) we propose a novel InSAR dataset for Slow SLIding area DEtection (ISSLIDE) with machine learning algorithms. The latter consists of manually annotated patches of generated interferograms over slow moving areas. (ii) We implement the segmentation of ISSLIDE interferograms with classical deep learning approaches. FCN, DeepLabV3 and U-Net-like architectures are explored to serve as baseline for future works. To the best of our knowledge, this is the first dataset adapted to machine learning and targeting slow sliding area detection.