Buildings, as fundamental man-made structures in urban environments, serve as
crucial indicators for understanding various city function zones. Rapid
urbanization has raised an urgent need for efficiently surveying building
footprints and functions. In this study, we proposed a semi-supervised
framework to identify every building's function in large-scale urban areas with
multi-modality remote-sensing data. In detail, optical images, building height,
and nighttime-light data are collected to describe the morphological attributes
of buildings. Then, the area of interest (AOI) and building masks from the
volunteered geographic information (VGI) data are collected to form sparsely
labeled samples. Furthermore, the multi-modality data and weak labels are
utilized to train a segmentation model with a semi-supervised strategy.
Finally, results are evaluated by 20,000 validation points and statistical
survey reports from the government. The evaluations reveal that the produced
function maps achieve an OA of 82% and Kappa of 71% among 1,616,796 buildings
in Shanghai, China. This study has the potential to support large-scale urban
management and sustainable urban development. All collected data and produced
maps are open access at this https URL

本文提出了一种半监督框架，利用多模态遥感数据在大规模城市区域中识别每个建筑物的功能。研究结果表明，在中国上海的 1,616,796 座建筑中，所生成的功能地图实现了 82% 的 OA 和 71% 的 Kappa，具有支持大规模城市管理和可持续城市发展的潜力。

利用多模态遥感数据在大规模城市区域中识别每个建筑物的功能

Identifying every building's function in large-scale urban areas with  multi-modality remote-sensing data

Being able to identify functions of interest in cross-architecture software
is useful whether you are analysing for malware, securing the software supply
chain or conducting vulnerability research. Cross-Architecture Binary Code
Similarity Search has been explored in numerous studies and has used a wide
range of different data sources to achieve its goals. The data sources
typically used draw on common structures derived from binaries such as function
control flow graphs or binary level call graphs, the output of the disassembly
process or the outputs of a dynamic analysis approach. One data source which
has received less attention is binary intermediate representations. Binary
Intermediate representations possess two interesting properties: they are cross
architecture by their very nature and encode the semantics of a function
explicitly to support downstream usage. Within this paper we propose Function
as a String Encoded Representation (FASER) which combines long document
transformers with the use of intermediate representations to create a model
capable of cross architecture function search without the need for manual
feature engineering, pre-training or a dynamic analysis step. We compare our
approach against a series of baseline approaches for two tasks; A general
function search task and a targeted vulnerability search task. Our approach
demonstrates strong performance across both tasks, performing better than all
baseline approaches.

通过结合长文档转换器和中间表示的使用，本文提出了一种能够进行跨体系结构功能搜索的模型，无需手动特征工程、预训练或动态分析步骤。我们将该方法与一系列基准方法进行了对比，在一般函数搜索任务和有针对性的漏洞搜索任务中，我们的方法表现出强大的性能，优于所有基准方法。