More than half of the world's roads lack adequate street addressing systems. Lack of addresses is even more visible in daily lives of people in developing countries. We would like to object to the assumption that having an address is a luxury, by proposing a generative address design that maps the world in accordance with streets. The addressing scheme is designed considering several traditional street addressing methodologies employed in the urban development scenarios around the world. Our algorithm applies deep learning to extract roads from satellite images, converts the road pixel confidences into a road network, partitions the road network to find neighborhoods, and labels the regions, roads, and address units using graph- and proximity-based algorithms. We present our results on a sample US city, and several developing cities, compare travel times of users using current ad hoc and new complete addresses, and contrast our addressing solution to current industrial and open geocoding alternatives.
Automating the generation of maps is extensively studied in the urban procedural modeling world @cite_10 @cite_23 @cite_21 @cite_9 @cite_7 , creating detailed and structurally realistic models, but none of them are applicable as a synthesis method on real world. On the other hand, inverse procedural modeling approaches @cite_30 process real-world data for generative representations. We follow this last path and rely on satellite imagery as the input of our synthesis approach.
@cite_30 @cite_7 @cite_9 @cite_21 @cite_23 @cite_10
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We present an interactive system for synthesizing urban layouts by example. Our method simultaneously performs both a structure-based synthesis and an image-based synthesis to generate a complete urban layout with a plausible street network and with aerial-view imagery. Our approach uses the structure and image data of real-world urban areas and a synthesis algorithm to provide several high-level operations to easily and interactively generate complex layouts by example. The user can create new urban layouts by a sequence of operations such as join, expand, and blend without being concerned about low-level structural details. Further, the ability to blend example urban layout fragments provides a powerful way to generate new synthetic content. We demonstrate our system by creating urban layouts using example fragments from several real-world cities, each ranging from hundreds to thousands of city blocks and parcels. We propose a framework that enables adding intuitive high level control to an existing urban procedural model. In particular, we provide a mechanism to interactively edit urban models, a task which is important to stakeholders in gaming, urban planning, mapping, and navigation services. Procedural modeling allows a quick creation of large complex 3D models, but controlling the output is a well-known open problem. Thus, while forward procedural modeling has thrived, in this paper we add to the arsenal an inverse modeling tool. Users, unaware of the rules of the underlying urban procedural model, can alternatively specify arbitrary target indicators to control the modeling process. The system itself will discover how to alter the parameters of the urban procedural model so as to produce the desired 3D output. We label this process inverse design. Urban models are key to navigation, architecture and entertainment. Apart from visualizing facades, a number of tedious tasks remain largely manual (e.g. compression, generating new facade designs and structurally comparing facades for classification, retrieval and clustering). We propose a novel procedural modelling method to automatically learn a grammar from a set of facades, generate new facade instances and compare facades. To deal with the difficulty of grammatical inference, we reformulate the problem. Instead of inferring a compromising, one-size-fits-all, single grammar for all tasks, we infer a model whose successive refinements are production rules tailored for each task. We demonstrate our automatic rule inference on datasets of two different architectural styles. Our method supercedes manual expert work and cuts the time required to build a procedural model of a facade from several days to a few milliseconds. We propose a novel grammar-driven approach for reconstruction of buildings and landmarks. Our approach complements Structure-from-Motion and image-based analysis with a 'inverse' procedural modeling strategy. So far, procedural modeling has mostly been used for creation of virtual buildings, while the inverse approaches typically focus on reconstruction of single facades. In our work, we reconstruct complete buildings as procedural models using template shape grammars. In the reconstruction process, we let the grammar interpreter automatically decide on which step to take next. The process can be seen as instantiating the template by determining the correct grammar parameters. As an example, we have chosen the reconstruction of Greek Doric temples. This process significantly differs from single facade segmentation due to the immediate need for 3D reconstruction.