Renovating existing buildings is essential for climate impact. Early-phase renovation planning requires simulations based on thermal 3D models at Level of Detail (LoD) 3, which include features like windows. However, scalable and accurate identification of such features remains a challenge. This paper presents the Scalable Image-to-3D Facade Parser (SI3FP), a pipeline that generates LoD3 thermal models by extracting geometries from images using both computer vision and deep learning. Unlike existing methods relying on segmentation and projection, SI3FP directly models geometric primitives in the orthographic image plane, providing a unified interface while reducing perspective distortions. SI3FP supports both sparse (e.g., Google Street View) and dense (e.g., hand-held camera) data sources. Tested on typical Swedish residential buildings, SI3FP achieved approximately 5% error in window-to-wall ratio estimates, demonstrating sufficient accuracy for early-stage renovation analysis. The pipeline facilitates large-scale energy renovation planning and has broader applications in urban development and planning.

Despite the technological advancements in the construction and surveying sector, the inspection of salient features like windows in an under-construction or existing building is predominantly a manual process. Moreover, the number of windows present in a building is directly related to the magnitude of deformation it suffers under earthquakes. In this research, a method to accurately detect and count the number of windows of a building by deploying an Unmanned Aerial Vehicle (UAV) based remote sensing system is proposed. The proposed two-stage method automates the identification and counting of windows by developing computer vision pipelines that utilize data from UAV's onboard camera and other sensors. Quantitative and Qualitative results show the effectiveness of our proposed approach in accurately detecting and counting the windows compared to the existing method.

There's a huge disadvantage of sliding windows detection which is the Computational cost, because we're cropping out so many different square regions in the image and running each of them independently through a \(convnet \). If we use the very course stride, a very big stride, very big step size, then that would reduce the number of windows we need to pass through the \(convnet \), but that coarser granularity may hurt performance, whereas if we use a very fine granularity or a very small stride then the huge number of all these little regions we're passing through the \(convnet \) means that there's a very high computational cost. Before the rise of neural networks people used to use much simpler classifiers, like a simple linear classifier overhand engineer features in order to perform object detection, and in that error because each classifier was relatively cheap to compute it was just a linear function, sliding windows detection ran properly, it was not a bad method, but with \(convnets \) now running a single classification task is much more expensive and sliding windows this way is infeasible slow. Unless we use a very fine granularity or a very small stride we end up not able to localize the objects that accurately within the image as well.

Please don't hesitate to get in touch if you have a topic you'd like to talk about or a project you want to present! - [masked]:) Anyline is going to sponsor free drinks at the beginning of the evening. Agenda: 7pm: Grab a welcome drink 7.30pm: Is the Singularity near? Where technology and AI could lead us: Facts, forecasts and disruptive projections. A talk by Michael Sprinzl Abstract: Baseline detection is still a challenging task for heterogeneous collections of historical documents. We present a novel approach to baseline extraction in such settings, turning out the winning entry to the ICDAR 2017 Competition on Baseline detection (cBAD).