This project aims to explore new research problems that can be solved by using image and video data analysis. This project will be developed in collaboration with the Lisbon City Council (CML) and addresses challenges that CML launched for the scientific community. The area topic to be addressed is computer vision for knowledge extraction based on aerial and street-level city imagery. We propose a new multi-spatial scale urban fabric dataset and a novel convolutional neural network solution for urban fabric classification tasks. Considering the challenges launched by CML and the already established collaboration between Iscte and CML, this project proposes to explore solutions to a set of urban classification problems by using video and image analysis. In this topic the goal is to identify features of the city shape, namely: i) to estimate the existence of unregistered greenspaces, as green rooftops (since CML is not currently aware of most green rooftops existing in Lisbon) and green back yards (not registered in the city council); ii) to estimate the height and deployment shape and area of Lisbon’s buildings, which will enable to develop an automatic and constantly updated tri-dimensional urban fabric map for Lisbon; iii) to estimate and automatically classify the conservation state of buildings’ facades; iv) among others. Such a methodology will be initially based on the set of aerial and street-level images obtained from CML (during the period of the scholarship). We are also considering exploiting existing city plans freely available at OpenStreetMaps to boost the convolutional neural network's training process, as well as to improve its inference performance. In a second phase, we will explore the use of other types of images such as from google street viewer and the ones directly collected by cameras in drones or city council vehicles (e.g., garbage trucks). For this second phase, we envision submitting a proposal to competitive funding and propose this topic...

This operation aims to improve the service provided by the Polícia Judiciária (PJ) to citizens by automating the operational process of cataloguing photographic images and comparing them with the existing image bank with tens of thousands of images, implementing a prototype that uses the latest Artificial Intelligence (AI) approaches. In particular, an innovative solution will be created for the ballistics area, identified by the PJ as a priority area, which will allow to identify with a high rate of success and speed the correspondence between projectile and possible type of weapon used, reducing the effort and intervention time of specialized human resources, which will have an impact in the process of evidence production and consequently will make Portugal a fairer country. The solution to be developed at ISCTE consists in the implementation of a specific classification model, based on Siamese neural networks, which allows the identification of weapon models from images of the casings of deflagrated projectiles (ballistic classification system). The training and testing of the model will make use of a previously created image database containing a large number of images of deflated projectile casings.