Hard HItters

https://docs.google.com/presentation/d/1jPV3-H4NvFZE1XEmH2vWiXW-nUTzqj_y/edit?usp=sharing&ouid=104631917799161057783&rtpof=true&sd=true

https://colab.research.google.com/drive/18sIwd-582-u5SpC_myAasgR6ITIxcB5B

The project uses space image data to show how the rapid urbanization and unethical land conversion has been negatively affecting our farmlands.

Oh. Look at that squiggly line in the box! Is that a river? Is it just me or it's starting to grow thinner? See? It's not just farmlands. We can use the tech to check for quarries, river changes, drying lakes. The possibilities are endless.

TECHNOLOGY USED

1. Image Registration: Alignment of Multiple Images acquired at separate times or different sensors.

2. Color Detection/Edge Detection: Compare images by analyzing the changes in the edges of colors or based on the volume of green/not green pixels in the image.

WHAT'S NEXT FOR THIS IDEA?

We still haven't found a concrete use for this data, but if you think about it, just seeing what's been happening could be an eye opener for us. Just knowing what really is happening can spark change such as:

• Motivate congress to pass laws prohibiting unethical conversion of farmlands.
• Raising awareness on farmers struggles.
• Empower the general population to be more assertive of big corporations.
• Force transparency in government and private sector.
• Drive people into making better choices in leaders.
• Encourage meaningful conversation about socio-economic issues.

Various Copernicus - Sentinel Missions

We can use high quality Sentinel Earth Images pointing to the Philippines

https://earth.esa.int/eogateway

SEDAC - Global Human Modification of Terrestrial Systems

To provide a cumulative measure of human modification of terrestrial lands based on modeling the physical extents of 13 anthropogenic stressors and their estimated impacts using spatially-explicit global data sets.

Poverty Report: Philippine Statistics Authority

Farmers, fisherfolks, individuals residing in rural areas and children who belong to families with income below the official poverty thresholds posted the highest poverty incidences among the basic sectors

https://psa.gov.ph/poverty-press-releases/nid/162541

Our NASA Space Apps experience made us think outside of the box. Leaning towards solution that can help us protect the future of our farmlands. As the world progress, we often turn our green spaces into commercialized or highly urbanized area. Using this solution, we can consider ethical and positive green space conversion. Let our green space live, support the farmers.

We realized that leveraging space data is pretty new to us, however, we are all familiar with satellite images. We know that we can actually use those images to see what changes are happening to our farmlands, from the sky. So what we did is we tried learning how image processing technologies can detect those changes over time. What we focused on initially is experimenting on finding the best method to do this, in addition to creating for the a positive impact, not just to farmers, but to society as a whole.

We still have a lot to learn, but we think that this project is worth it.

Here's some news and articles that inspired us:

1. Impacts of Agricultural Land Acquisition for Urbanization on Agricultural Activities of Affected Households: A Case Study in Huong Thuy Town, Thua Thien Hue Province, Vietnam
2. Many Farms Lost to Land Conversion
3. Urbanization in the Philippines and Its Influence on Agriculture
4. Urbanization and its implications for food and farming

Some Science References:

1. Farid, H. and Simoncelli, E. P., “Differentiation of discrete multidimensional signals”, IEEE Transactions on Image Processing 13(4): 496-508, 2004. DOI:10.1109/TIP.2004.823819

2. Wikipedia, “Farid and Simoncelli Derivatives.” Available at: <https://en.wikipedia.org/wiki/Image_derivatives#Farid_and_Simoncelli_Derivatives

3. https://en.wikipedia.org/wiki/Sobel_operator#Alternative_operators

4. B. Jaehne, H. Scharr, and S. Koerkel. Principles of filter design. In Handbook of Computer Vision and Applications. Academic Press, 1999.

5. https://en.wikipedia.org/wiki/Prewitt_operator

6. Xie, Yonghong, and Qiang Ji. “A new efficient ellipse detection method.” Pattern Recognition, 2002. Proceedings. 16th International Conference on. Vol. 2. IEEE, 200

7. Meijering, E., Jacob, M., Sarria, J. C., Steiner, P., Hirling, H., Unser, M. (2004). Design and validation of a tool for neurite tracing and analysis in fluorescence microscopy images. Cytometry Part A, 58(2), 167-176. DOI:10.1002/cyto.a.20022

8. Sato, Y., Nakajima, S., Shiraga, N., Atsumi, H., Yoshida, S., Koller, T., …, Kikinis, R. (1998). Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. Medical image analysis, 2(2), 143-168. DOI:10.1016/S1361-8415(98)80009-1

9. Frangi, A. F., Niessen, W. J., Vincken, K. L., & Viergever, M. A. (1998, October). Multiscale vessel enhancement filtering. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 130-137). Springer Berlin Heidelberg. DOI:10.1007/BFb0056195

10. Ng, C. C., Yap, M. H., Costen, N., & Li, B. (2014, November). Automatic wrinkle detection using hybrid Hessian filter. In Asian Conference on Computer Vision (pp. 609-622). Springer International Publishing. DOI:10.1007/978-3-319-16811-1_40

#food #security #sdg2 #farmland #urbanization #human #rights #hunger

This project has been submitted for consideration during the Judging process.