2021-11-20 –, Room 2
Language: English
This talk is generally about the utility of open street level imagery in local development, with a focus on environmental issues, namely Municipal Solid Waste (MSW)
Initially, the work will focus on preparing a demo research model for the use case of Municipal Solid Waste MSW Detection With Machin Learning using Street-Level imagery open data collected by OpenStreetMap Libya contributors and Kartaview volunteer teams.
As a result of the activity of the of street level imagery contributors, a huge database of imagery was cultivated, sifting through the imagery to validate quality we noticed a huge environmental issue, which is Municipal Solid Waste on the side of the roads within and at the outskirts of cities, so we had to take action regarding the issue by creating a working model to automatically detect MSW and highlight the areas and question, to help aid local government and bring attention to the environmental disaster.
General concept
Initially, the work will focus on preparing a demo research model for the use case of Municipal Solid Waste MSW Detection With Machin Learning using Street-Level imagery open data collected by OpenStreetMap Libya contributors and Kartaview volunteer teams.
This use case could be developed to be one of the National Spatial Data Infrastructure layers in Libya. It could also provide an Urban Support system or tool for smart city management.
In addition to the environmental gains, public health, sanitation, and The general appearance, many business models could benefit from the usage of this model.
The added value of the research model
The principal added value of the model is to highlight the power and the importance of the contribution of citizens and the social engagement in the phase of the spatial data collection to build the base map and support the national spatial data infrastructure.
Another added value of the research model is to explore the benefits of open source tools and crowdsourced spatial datasets.
Finally is to valorize the efforts of collective intelligence-based models in a practical and actual use case that could contribute to finding reliable solutions for the Municipal Solid Waste Management, which challenging nowadays.
