DeepAI AI Chat
Log In Sign Up

An Enriched Automated PV Registry: Combining Image Recognition and 3D Building Data

by   Benjamin Rausch, et al.

While photovoltaic (PV) systems are installed at an unprecedented rate, reliable information on an installation level remains scarce. As a result, automatically created PV registries are a timely contribution to optimize grid planning and operations. This paper demonstrates how aerial imagery and three-dimensional building data can be combined to create an address-level PV registry, specifying area, tilt, and orientation angles. We demonstrate the benefits of this approach for PV capacity estimation. In addition, this work presents, for the first time, a comparison between automated and officially-created PV registries. Our results indicate that our enriched automated registry proves to be useful to validate, update, and complement official registries.


page 1

page 2

page 3

page 4


Localization in Aerial Imagery with Grid Maps using LocGAN

In this work, we present LocGAN, our localization approach based on a ge...

Deep cross-domain building extraction for selective depth estimation from oblique aerial imagery

With the technological advancements of aerial imagery and accurate 3d re...

SpaceNet: A Remote Sensing Dataset and Challenge Series

Foundational mapping remains a challenge in many parts of the world, par...

Multi-view deep learning for reliable post-disaster damage classification

This study aims to enable more reliable automated post-disaster building...

Improving Building Segmentation for Off-Nadir Satellite Imagery

Automatic building segmentation is an important task for satellite image...

On procedural urban digital twin generation and visualization of large scale data

The desired outcome for urban digital twins is an automatically generate...

Monitoring War Destruction from Space: A Machine Learning Approach

Existing data on building destruction in conflict zones rely on eyewitne...