Der Earth Control Measure Evaluator des PUB: Entwicklung eines Prototyps mit maschinellem Lernen

• Bei der Wasserbehörde von Singapur gehen jedes Jahr über 1500 Zeichnungen ein, deren manuelle Prüfung Zeit und ein geübtes Auge braucht.

• Die Herausforderung? Wie lässt sich mit maschinellem Lernen der Zeitaufwand bei der manuellen Prüfung reduzieren, ohne Fehler zu übersehen?

• Hier erfahren Sie, wie in der Cloud ein vollständiger Prototyp entwickelt und für die Erkennung bestehender Zeichnungen und darin vorkommender häufiger Fehler trainiert wurde, um den Gesamtprozess zu optimieren.

Situated near the equator, Singapore has a typically tropical climate with abundant rainfall. It rains an average of 167 days a year, with an annual total rainfall of around 2,500mm. During heavy rainfall, silt can be washed from exposed earth surfaces in construction sites into waterways. Good Earth Control Measures (ECM) implemented in construction sites prevent silty runoff from being washed into waterways, keeping them clean and beautiful.

PUB, Singapore’s national water agency, receives more than 1,500 drawing submissions per year seeking approval for construction works that require Earth Control Measures (ECM) to be implemented. This process requires time as well as an experienced eye to manually review the drawings and ensure that the ECMs are properly designed.

Scale implementation with speed and accuracy

Over a span of four months, the team built a corresponding end-to-end prototype incorporating:

• Multiple models of computer vision (object detection and instance segmentation) trained on past ECM submissions to recognise key design elements
• Optical character recognition to extract relevant text
• Rule engine to check compliance based on the recognitions and a prototypical user interface to interact with PUB officers.

Designed for sustainable growth

Thorough analysis of the algorithms’ performance has identified the working scenarios and their shortcomings. The prototype is deployed on Amazon Web Services (AWS), allowing to further probe the models with new data. In combination, these findings allow to adjust the business case and steer the submission format towards desired standardisation levels.

More about the Automated Earth Control Measure Submission Evaluator innovation challenge.