Better Hackathon 2020 - WFP - Enhancing Agricultural Mapping With BETTER Pipelines
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Better Hackathon 2020 - WFP - Enhancing Agricultural Mapping With BETTER Pipelines
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As part of the final BETTER Hackathon, project partners prepared 4 hackathon exercises. WFP organised this exercise as the challenge promoter for the Food Security thematic area. This open exercise featured the use of Binder and purposely provided cloud resources. Participants were expected to be familiar with the Jupyter environment (Python 3) and the most common EO libraries (e.g. GDAL) and were guided to use their favourite approach (e.g. pixel-based or object-based classification) to derive a crop type map for the region using the following combinations of datasets: S2 unfiltered – benchmark, S2 filtered, S2 unfiltered + SAR, S2 filtered + SAR. Libraries used included Rasterio / GDAL, pandas + numpy, scipy, numba, keras / tensorflow / opencv. The recorded part includes the introduction of the exercise in the context of the BETTER project.
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Better Hackathon 2020 - WFP - Enhancing Agricultural Mapping With BETTER Pipelines
- 1. Enhancing Agricultural Mapping with BETTER Pipelines WFP - BETTER Hackathon – 23rd October 2020 Big-data Earth observation Technology and Tools Enhancing Research and development http://ec-better.eu This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement no 776280
- 2. Relevance for WFP Changes in Agricultural Area under Conflict Crop type mapping in conflict areas: Quantify losses of cropland and post-conflict regeneration. First time cropland baseline (e.g. South Sudan) Technical assistance to governments: In close cooperation with Min of Agriculture. Smallholder support systems: Assess seasonal performance potential, likelihood of meeting procurement targets, changes staple-cash crop in area of intervention. WFP Project Interventions: Agriculture oriented FFA projects, resilience building interventions, index insurance. BETTER | Enhancing Agricultural Mapping with BETTER Pipelines
- 3. Sentinel-2 Data Assimilation Cloud Storage and Processing Centre Crop Type Map ONA Platform Assimilation, Quality Control, Conversion Sen2Agri System BETTER | Enhancing Agricultural Mapping with BETTER Pipelines Crop Type Ground Data Collection
- 4. BETTER | Enhancing Agricultural Mapping with BETTER Pipelines Preferred option: Geo-referenced perimeters Field Data Large Fields along roads: Geo-referenced Transects Field Capture Office Completion Huge Fields: Single Points / Corners
- 6. http://ec-better.eu BETTER | Enhancing Agricultural Mapping with BETTER Pipelines Sen2Agri has served its purpose well. However: • It only uses S2 and L8, but no SAR. • There is a module for atmospheric correction but significant atmospheric interference may still have an impact on the quality of the classification WFP ClEO Team has implemented a flexible, data-driven, pixel-optimized filter: • So far routinely applied to medium resolution data, NDVI and LST – smooth, clean images without mosaicking artifacts or cloud inflicted gaps. • Can we apply the same to Sentinel-2 data? Good for hazard mapping, but is there a benefit for crop type (or more generally land cover) classification
- 7. http://ec-better.eu Jan-Oct 2019 BETTER | Enhancing Agricultural Mapping with BETTER Pipelines
- 8. http://ec-better.eu BETTER | Enhancing Agricultural Mapping with BETTER Pipelines Filter behavior is modulated by two parameters: • S – controls the amount of smoothing that is applied. The smaller the S, the closer to the original data the output is. At the extremes the output will be a polynomial of degree n (usually 1 or 2). • S can be optimized (CV, V-curve)
- 9. http://ec-better.eu BETTER | Enhancing Agricultural Mapping with BETTER Pipelines Filter behavior is modulated by two parameters: • p – controls the asymmetry of the filtered output and is a value between 0 and 1. When p is 0.5, filtered output balances noise. As p tends to 1 the filter fits the output to the higher envelope of the signal. As p tends to 0 the filter fits to the lower envelope of the signal (baseline) . • NDVI: use p > 0.5 • Reflectances: use p < 0.5 • p is not optimized, expert judgement
- 10. http://ec-better.eu BETTER | Enhancing Agricultural Mapping with BETTER Pipelines Data Set: • 8800 time series of raw and smoothed Sentinel-2 reflectances and SAR • Smoothing: S = optimized btn 3.0-4.0 and p=0.2 (p=0.3 for Band 8, 11 and 12) • Nature: pixels selected inside each agronomic data sample, 25m distance • Field data: Crop field outlines, collected early October 2020 (pre-harvest) • Location: Adamawa State, NE Nigeria • Dates: Time series from May to October 2020 • Crop Types: Maize, Millet, Sorghum, Beans, Soya and their mixes. Also fallow (unused) and No-ID (abandoned field) • 7000 labelled data and 1800 unlabelled
- 11. http://ec-better.eu BETTER | Enhancing Agricultural Mapping with BETTER Pipelines The Exercise: • Devise a classifier for crop types to be run with the various datasets provided and results compared • You are free to choose combinations of data. E.g. • Raw S2 vs Filtered S2 • Raw S2 + SAR vs Filtered S2 • SAR vs Filtered S2 • … whatever • You can team up if you want • Produce your predictions for the unlabeled data and submit Google Hangouts for Bilateral Support: https://meet.google.com/mde-useo-qrb
- 12. Enjoy the Work! Thank You! http://ec-better.eu