Situation

City administrations and departments are faced with the need to make strategic decisions about territorial development based on vast amounts of diverse data: human mobility flows, social and commercial infrastructure utilization, and transport accessibility. Traditional analytical tools were unable to cope with this volume, and predictive models were overly simplified.

Action

A geoinformation ML system for urban modeling and master planning was developed. It is based on a hybrid architecture combining geoanalytics and machine learning algorithms. The system processes large data sets on population mobility, infrastructure visitation, and urban planning parameters, and builds predictive models for the load on schools and clinics (and 10+ other social facilities) and the impact of new residential development. The algorithms automatically identify hidden patterns and enable interactive testing of various development scenarios ("what if...").

Technologies

ML technology and geoanalytics with BigData
Visualization: adaptive web service

Result

The system has been implemented by city project offices and development teams. It allows:

• to improve the accuracy of social impact forecasts (validated using historical data);
• to model up to dozens of development options and select the optimal one based on a set of criteria;
• to ensure transparency and validity of urban planning decisions for all stakeholders.

MY CONTRIBUTIONSProject + product management · Development team coordination · Roadmap and resource management · Data collection · Testing · Client demonstrations · Results acceptance

情况

城市管理部门和职能部门在制定区域发展战略决策时，需要处理海量多元数据：人口流动轨迹、社会与商业基础设施利用率、交通可达性等。传统分析工具难以应对这一数据量级，而预测模型又过于简化。

行动

项目团队开发了一套面向城市建模与总体规划的地理信息机器学习系统，采用融合地理分析与机器学习算法的混合架构。该系统处理人口流动、基础设施使用及城市规划参数等大规模数据集，为学校、诊所（以及 10 余种其他社会设施）的负载及新住宅开发的影响构建预测模型。算法自动识别隐藏规律，并支持各类发展方案的交互式测试（"如果……会怎样"）。

技术

机器学习技术与大数据地理分析
可视化：自适应 Web 服务

成果

该系统已由城市项目办公室和开发团队落地应用，可实现：

• 提升社会影响预测的准确性（经历史数据验证）；
• 对数十种开发方案进行建模，并基于多维指标筛选最优方案；
• 确保所有相关方对城市规划决策的透明度与可靠性。

我的贡献项目与产品管理 · 开发团队协调 · 路线图与资源管理 · 数据收集 · 测试 · 客户演示 · 成果验收

Situation

Businesses regularly face administrative fines for violating advertising and signage regulations. The regulations are complex, context-dependent, and vary across buildings, streets, and districts. Moscow entrepreneurs lack tools to check their planned or existing signs in advance and avoid penalties.

Action

A web service based on an expert system using computer vision was developed. Numerous complex, context-dependent rules (building type, street status, adjacent signs, historical zones) were formalized into a single automatic verification algorithm. The user uploads the sign's parameters (or specifies the address), and the system returns a result in seconds: whether the structure complies with city regulations.

Technologies

CV models for classification and segmentation
OCR for text analysis
A rule-based engine
Visualization: adaptive web service

Result

The service has become a self-monitoring tool for entrepreneurs: they can check both existing and planned signs before receiving a fine. The system identifies specific violations, provides recommendations for correcting them, and informs about the availability of approved regulations at a given address. Now, you can check your sign in just two minutes, not two weeks.

MY CONTRIBUTIONSProject + product management · Development team coordination · Roadmap and resource management · Data collection · Testing · Client demonstrations · Results acceptance

情况

企业常因违反广告牌及标识法规而面临行政罚款。相关法规繁杂、依情境而定，且因建筑、街道和区域的不同而有所差异。莫斯科的企业主缺乏事先核验计划或现有标识、规避处罚的工具。

行动

项目团队基于计算机视觉专家系统开发了一套 Web 服务，将众多复杂的情境相关规则（建筑类型、街道状态、相邻标识、历史保护区）形式化为单一的自动核验算法。用户上传标识参数（或指定地址），系统在数秒内返回结果：该构件是否符合城市法规要求。

技术

用于分类与分割的 CV 模型
用于文本分析的 OCR
基于规则的引擎
可视化：自适应 Web 服务

成果

该服务已成为企业主的自查工具：无论是现有标识还是计划中的标识，均可在收到罚款前完成核验。系统指出具体违规项，提供整改建议，并告知指定地址是否有已批准的规范可供参考。现在，核验一块标识只需两分钟，而非两周。

我的贡献项目与产品管理 · 开发团队协调 · 路线图与资源管理 · 数据收集 · 测试 · 客户演示 · 成果验收