Revamping Real Estate investments with a Neural Network pipeline for image recognition and knowledge extraction from floor plans and planning applications

规划应用程序中的结构建筑数据对于商业真实的房地产和投资决策至关重要，无论是购买，出售还是使用房产。投资回报和绩效与访问高质量规划数据的速度直接相关。这个高度创新的项目提出了使用人工智能，特别是机器学习和自然语言处理自动提取规划信息的任务。Honest AI技术搜索和分析数千张图像和PDF，通过上下文理解搜索查询的内容，分析并将这些信息与提取的图像和PDF中的相关数据进行匹配，并将答案反馈给用户。该系统将根据用户以前的行为和反馈，了解用户的信息需求，从而促进商业真实的房地产公司知识工作者的工作流程/任务自动化。该解决方案将作为附加功能提供，可直接在我们以前开发的商业真实的房地产数据企业搜索平台中访问。它将帮助商业真实的房地产投资者、承销商和开发商加速房地产分析，避免手动将数据输入公司系统和分析工具，使他们能够专注于更高价值的任务。它将通过减少手动任务所花费的时间来节省高达30%的成本。除此之外，我们预计运营风险将大幅降低，这将降低错误成本，并提高所有利益相关者决策的透明度。

Structural building data inside planning applications is crucial for commercial real estate and for investment decisions, whether to buy, sell or purpose a property. Investment returns and performance directly correlate to the speed of access to quality planning data. This highly innovative project proposes the automation of extraction tasks of planning information using artificial intelligence, especially machine learning and natural language processing. Honest AI technology searches and analyses thousands of images and PDFs, and by contextual understanding the content of the search query, it analyses and matches this information with relevant data in extracted images and PDFs and gives the answer back to the user. The system will facilitate the workflow/task automation of knowledge workers in commercial real estate firms by understanding the information needs of the users based on previous behaviour and feedback given. The solution, accessed directly within our previously developed enterprise search platform for commercial real estate data, will be offered as an add-on feature. It will help commercial real estate investors, underwriters and eveloperers to accelerate property analysis as well as avoid manual data entry into corporate systems and analysis tools, allowing them to focus on higher-value tasks. It will generate significant cost savings of up to 30% through the reduction of the time spent on manual tasks. In addition to that, we expect significant operational risk reduction, which will lead to reduced cost of mistakes as well as additional transparency in decision making across all stakeholders.