Project Sunroof estimates the solar potential of residential buildings using
high quality aerial data. That is, it estimates the potential solar energy (and
associated financial savings) that can be captured by buildings if solar panels
were to be installed on their roofs. Unfortunately its coverage is limited by
the lack of high resolution digital surface map (DSM) data. We present a deep
learning approach that bridges this gap by enhancing widely available
low-resolution data, thereby dramatically increasing the coverage of Sunroof.
We also present some ongoing efforts to potentially improve accuracy even
further by replacing certain algorithmic components of the Sunroof processing
pipeline with deep learning.

使用深度学习方法增强低分辨率数据以提高 Project Sunroof 对住宅建筑太阳能电池板潜能评估准确性和覆盖范围。

利用航空数据估算住宅太阳能潜力

Estimating Residential Solar Potential Using Aerial Data

We present the Dayton Annotated LiDAR Earth Scan (DALES) data set, a new
large-scale aerial LiDAR data set with over a half-billion hand-labeled points
spanning 10 square kilometers of area and eight object categories. Large
annotated point cloud data sets have become the standard for evaluating deep
learning methods. However, most of the existing data sets focus on data
collected from a mobile or terrestrial scanner with few focusing on aerial
data. Point cloud data collected from an Aerial Laser Scanner (ALS) presents a
new set of challenges and applications in areas such as 3D urban modeling and
large-scale surveillance. DALES is the most extensive publicly available ALS
data set with over 400 times the number of points and six times the resolution
of other currently available annotated aerial point cloud data sets. This data
set gives a critical number of expert verified hand-labeled points for the
evaluation of new 3D deep learning algorithms, helping to expand the focus of
current algorithms to aerial data. We describe the nature of our data,
annotation workflow, and provide a benchmark of current state-of-the-art
algorithm performance on the DALES data set.

介绍了 Dayton Annotated LiDAR Earth Scan 数据集，该数据集是一个大规模的航空 LiDAR 数据集，涵盖 8 种物体类别的超过半亿个手动标注点，该数据集可以用于评估新的三维深度学习算法的性能，扩展当前算法的航空数据的研究重点。