Lima is facing rapid urban growth, including a rapid expansion of informal areas, mainly taking place within three peripheral cones. Most of the studies on that subject focused in general on informal settlements. Yet in this paper, we focus on two different informal types, graveyards and housing. They are experiencing complex, intertwined development dynamics due to a lack of land for housing and burials, causing social and public health problems. Housing invasions on burial grounds have never been systematically investigated. Yet, while challenging due to their morphological similarity, the detection of boundaries between graveyards and neighbouring and sometimes invading informal housing is essential, e.g., to prevent the spread of diseases. This study aims to distinguish those similar urban structures of which the visual features are very alike (e.g., rectangular shapes, same colours, organic organization). We used state-of-the-art Fully Convolutional Networks (FCNs) with dilated convolution of increasing spatial kernels to acquire features of deep level of abstraction on Pleiades satellites images. We found that such neural networks can reach a good level in mapping both informal developments with a F1-score of 0.819. Effective monitoring of such developments is important to inform planning and decision-making processes to allow interventions at critical locations.