Neospatial data approaches 🧙

1Neospatial 🛰️ 🌐 ☁️¶

1.1Background¶

1.2Audience¶

1.3Chapters¶

The book contains the following chapters

1. Introduction. An introduction to the concept of neospatial.
2. CNG. Cloud-native geospatial concepts and applications.
3. Optimization. Mathematical algorithms for optimizing variables for forest health and conservation.
4. Machine Learning. Machine learning algorithms for land use land cover change integrated with remote sensing applictaions.
5. AI. Practical, useful, and accurate uses of AI in geospatial and cloud-native applications.
6. Lidar. Lidar collection and processing workflows using open source tools and python packages.
7. Drones. Using drones to collect local to landscape multispectral imagery.

1.4Map demo¶

Land, carbon, and biodiversity data for supply chain impact calculations are part of reducing negative drivers of land conversion [Gassert et al. (2023)]. Using data from Source Cooperative rendered through Leafmap, we demonstrate how quickly you can upload, analyze, and visualize cloud-native geospatial data at large scale.

# import dependencies
import leafmap.foliumap as leafmap
import os

# add data from deforestation carbon emissions (source.coop land, carbon, bd data)
deforest_hum_cog = 'https://data.source.coop/vizzuality/lg-land-carbon-data/deforest_by_human_lu_50km_1000m_cog.tif

# initialize map
m = leafmap.Map(center=[40, -100], zoom=4)
m

  Cell In[1], line 6
    deforest_hum_cog = 'https://data.source.coop/vizzuality/lg-land-carbon-data/deforest_by_human_lu_50km_1000m_cog.tif
                       ^
SyntaxError: unterminated string literal (detected at line 6)

1.5Takeaways¶

1.6Resources¶