uploaded geoai in martime blog

Dockerfile

@@ -38,6 +38,8 @@ RUN apt-get update && \
     apt-get clean && \
     rm -rf /var/lib/apt/lists/*
 
+RUN apt-get update && apt-get install -y python3-pip && pip3 install notedown
+
 # Switch back to spacevim user
 USER spacevim
 

blog-geoai/_posts/2023-10-21-GeoAI-In-Maritime-Logistics-blog.markdown

@@ -0,0 +1,26 @@
+---
+layout: post
+title:  "AI in Maritime Logistics and Supply Chain"
+date:  2023-10-21 22:41:32 +0800
+category: GeoAI 
+---
+
+The maritime industry, pivotal in transporting over 80% of global goods, is undergoing an intense technological evolution. As global supply chains become increasingly complex, the deployment of Artificial Intelligence (AI) and geospatial data science has surfaced as an indispensable tool in refining maritime operations. Singapore, a linchpin in the global maritime network, has recognized this potential. Positioned at the crossroads of major shipping routes, Singapore has always been at the maritime forefront. The country's investment and focus on integrating AI into its maritime infrastructure underline its commitment to maintaining its status as a leading global maritime hub.
+
+## Port Operations and AI  
+Deep learning models, trained on vast historical port data coupled with geospatial patterns of ship movements, guide predictive port operations.  Modern ports are evolving from mere docking points to data-driven hubs. The integration of AI with geospatial data enables ports to anticipate vessel movements better and manage port traffic. The Port of Rotterdam, for instance, benefits from this amalgamation, optimizing docking schedules based on predictive analytics and spatial patterns, ensuring efficient space utilization and reduced congestion.  
+
+## Route Optimization in Maritime Logistics  
+AI algorithms incorporate geospatial data, weather patterns, historical route efficiencies, and geopolitical situations to derive the most optimal maritime routes.  Route optimization is fundamentally a spatial problem. Geospatial data science, combined with AI, offers a comprehensive understanding of marine routes, identifying patterns and obstacles that might be missed by traditional methods. Maersk’s route optimization, powered by this symbiotic relationship, leads to safer and more efficient navigational decisions.  
+
+## Cargo and Container Management  
+Machine learning models use geospatial analytics on data from container movements, enabling precise container tracking and spatially efficient stacking.  Efficient cargo and container management are crucial in vast port areas. The combination of AI and geospatial insights provides ports with spatially efficient strategies for container placement, leading to faster retrieval and optimized storage. Port operators like DP World are harnessing this synergy for proactive space management and enhanced throughput forecasting.  
+
+## Forecasting and Demand Prediction  
+Using regression models, time series analysis, and geospatial clustering, ports can predict where demand will surge and prepare accordingly.  Forecasting in maritime logistics is not just about time but also about space. By integrating geospatial data science, ports can anticipate not just when, but precisely where demand will peak. Asian ports, using these enhanced methodologies, ensure resources are allocated optimally in both time and space dimensions.  
+
+## Maritime Supply Chain Visibility  
+Real-time data collection systems, merged with AI-powered geospatial analytics tools, offer enhanced insights into cargo status, potential delays, and spatial relationships between shipments.  In the maritime supply chain, understanding the spatial relationship between goods, their routes, and potential obstacles is paramount. Platforms like CargoSmart’s, fortified with geospatial analytics, provide stakeholders with a clearer spatial context, making tracking more intuitive and predictive.  
+
+## Conclusion 
+The confluence of AI and geospatial data science is ushering in a transformative era for maritime logistics. Singapore, with its forward-thinking initiatives, exemplifies the potential of this fusion. As maritime hubs globally recognize and embrace these technological advancements, the maritime industry's horizon promises unprecedented efficiency and precision.

blog-spatial-tool/_posts/2023-10-18-spatial-tool-blog-docker.markdown

@@ -13,15 +13,15 @@ Docker is a platform used to develop, ship, and run applications inside containe
 ## Why Use Docker for Geospatial Data Science?  
 1. **Reproducibility:** Docker containers ensure that geospatial analyses run consistently across different environments. 
 2. **Portability:** A Docker container developed on one machine can be shared and executed on any other machine that has Docker installed. 
-3. **Isolation:** Containers allow you to have isolated environments, ensuring that system-wide settings or installations don't interfere with your geospatial tools. 
+3. **Isolation:** Containers allow you to have isolated environments, ensuring that system-wide settings or installations don't interfere with your geospatial tools. nfujj3. **Isolation:** Containers allow you to have isolated environments, ensuring that system-wide settings or installations don't interfere with your geospatial tools. 
 4. **Version Control:** You can have multiple versions of software, libraries, and tools without conflicts.  
 
 ## Steps to Use Docker for Geospatial Data Science:  
 1. **Install Docker:** Follow the official documentation to [install Docker](https://docs.docker.com/get-docker/) on your machine. 
 
 2. **Find or Create a Geospatial Docker Image:**     - The Docker Hub contains many pre-built images suitable for geospatial analysis. If you are a R user, suggest to have a look at this [Rocker Project](https://rocker-project.org/images/). Once you get familiar with docker, no need to install R and all the geospatial packages in local machine anymore. Awesome! 
 
-3. **Run the Container:** Execute the Docker container using the image you've chosen. For instance, to run a GDAL Docker image:
+3. **Run the Container:** Execute the Docker container using the image you've chosen. For instance, to run a GDAL Docker image:nfujj3. **Run the Container:** Execute the Docker container using the image you've chosen. For instance, to run a GDAL Docker image:
 
 4. **Integrate Data and Analysis:** Bind mount your local geospatial data directory into the Docker container for easy access. This ensures that the containerized software tools can process your datasets.
 
@@ -36,4 +36,4 @@ Docker is a platform used to develop, ship, and run applications inside containe
 ## Conclusion  
 Docker brings ease and consistency to geospatial data science. By abstracting away the intricacies of software installation and environment setup, Docker allows data scientists to focus on the essence of their work: the analysis and extraction of insights from geospatial data. If you're in the geospatial domain and haven't explored Docker yet, it's high time you did!
 P.S. the current blog is written using spacevim docker image.
-  docker run -it -e DISPLAY=$DISPLAY -v /mnt/c/Users/Lenovo/.SpaceVim.d:/home/spacevim/.SpaceVim.d -v /mnt/c/Users/Lenovo/Desktop/hengshan/projects/hengshan.github.io:/home/spacevim/projects/hengshan.github.io --rm myspacevim nvim
+>  docker run -it -e DISPLAY=$DISPLAY -v /mnt/c/Users/Lenovo/.SpaceVim.d:/home/spacevim/.SpaceVim.d -v /mnt/c/Users/Lenovo/Desktop/hengshan/projects/hengshan.github.io:/home/spacevim/projects/hengshan.github.io --rm myspacevim nvim