Project Abstract

In NYC, sea level rise has led to a dramatic increase in flood risk, particularly in low-lying and coastal neighborhoods. Urban flood water can impede pedestrian and vehicle mobility, and also can contain a diverse array of contaminants, including fuels, raw sewage, and industrial/household chemicals. For this capstone project, the team will train, test and deploy computer vision (CV) and deep learning (DL) models for the detection of street flood events. Existing labelled datasets will be used for training. In addition, an unlabelled NYC street image dataset will be provided for labelling and training of a NYC centric model.

Project Description & Overview

In NYC, sea level rise has led to a dramatic increase in flood risk, particularly in low-lying and coastal neighborhoods. Urban flood water can impede pedestrian and vehicle mobility, and also contains a diverse array of contaminants, including fuels, raw sewage, and industrial/household chemicals.

The FloodNet project is interested in evaluating whether a longitudinally deployed fleet of CV flood sensors can monitor urban flooding events in real-time. This data can improve resiliency by (1) allowing residents to identify navigable transportation routes and make informed decisions to avoid exposure to flood water contaminants, and (2) informing city agencies in targeting flood control improvements through data-driven decision making.

The Capstone team will train, test and deploy CV/DL models for the detection of street flood events. Existing labelled datasets will be used for training. In addition, an unlabelled NYC street image dataset will be provided. The labelling strategy of this dataset will be determined by the team. Unsupervised or weakly supervised DL approaches could also be explored.

The team will work through three stages:

1. General flood detection model: The team will train and test a model built using existing labelled datasets. (40%)
2. Literature review on privacy and ethical concerns: The team will complete a review on CV ethics/privacy concerns in urban sensing. (10%) 
3. Data cleaning/labelling and training of NYC centric flood model: Data collected from NYC streets including flood and non-flood imagery will be cleaned and labelled, then used to build a NYC centric flood detection model. (50%)

Datasets

Existing labelled flood imagery datasets will be used in Stage 1 of the Capstone project. Stage 3 will involve the generation of a NYC centric dataset using existing unlabelled images collected from NYC streets in both flood and non-flood conditions.

Competencies

• Machine learning
  • Dimensionality reduction
  • Supervised learning
  • Semi-/Weak-supervised learning
  • Computer vision experience
• Good code and data management skills
• Python SciPy stack and PyTorch DL library
• Team technical experience
  • Python programming (required for >=2 team members)
  • Data processing pipelines
  • Documentation
• Data management experience
  • Privacy and data
  • Ethics

Learning Outcomes & Deliverables

The team will be using a broad range of urban analytics approaches that will result in proven abilities in: computer vision, remote sensing, data science, and machine learning.

The expected deliverables for each project stage are:

1. A model that operates with a given minimum performance level on the provided test data.
2. A literature review on the ethical and privacy concerns surrounding urban sensing and CV solutions.
3. The NYC centric model with performance levels exceeding a given threshold under varying real world conditions, including a new labelled urban flood dataset and associated tools for open sourcing on the data platform Zenodo.

All deliverables will be based around Jupyter notebooks and committed to a well documented public GitHub repository.