Browser-based light pollution simulator using real photometric data
Web AppHi HN — author here. iesna.eu is a browser-based ecosystem for working with photometric data: parsing standard luminaire files (LDT/EULUMDAT, IES LM-63, Oxytech, ATLA-S001), running design calculations against EN 13201 / ANSI/IES RP-8 / CJJ 45 / IES-IDA MLO, and (the part I
- Best for
- real-time light pollution visualization, geographic data integration for light pollution modeling
- Type
- Web App
- Score
- 30/100
- Best alternative
- PostHog
Capabilities2 decomposed
real-time light pollution visualization
Medium confidenceThis capability utilizes real photometric data to render light pollution effects in a browser environment. It employs WebAssembly for performance optimization, allowing complex calculations to be executed efficiently in the browser. The simulator integrates with geographic data to accurately model light pollution across different locations, providing a dynamic and interactive user experience.
Utilizes WebAssembly for high-performance rendering of light pollution effects directly in the browser, allowing for real-time interaction without server-side processing.
More interactive and responsive than traditional desktop simulations due to its browser-based architecture and real-time rendering capabilities.
geographic data integration for light pollution modeling
Medium confidenceThis capability integrates geographic data with photometric information to create accurate light pollution models. It uses geographic information system (GIS) principles to overlay light sources on maps, allowing users to visualize how different factors contribute to light pollution in specific areas. The integration is seamless, enabling users to input coordinates and receive immediate visual feedback.
Combines GIS principles with photometric data to provide a comprehensive view of light pollution effects, which is often lacking in other simulation tools.
Offers a more detailed and localized analysis compared to generic light pollution calculators that do not consider geographic context.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓urban planners assessing light pollution impact
- ✓environmental researchers studying light effects
- ✓educators teaching about light pollution
- ✓urban developers looking to mitigate light pollution
- ✓researchers studying environmental impacts
- ✓students learning about GIS and light pollution
Known Limitations
- ⚠Dependent on the availability of accurate photometric data, which may vary by location
- ⚠Performance may degrade on lower-end devices due to high computational demands
- ⚠Accuracy is contingent on the quality and resolution of geographic data used
- ⚠Limited to areas where photometric data is available
Requirements
Input / Output
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