You will get a Python Script that analyzes satellite imagery in Google Earth Engine
Project details
You will get a Python Script that automates Land Cover change detection analysis in an area using Satellite imagery and high quality maps and graphs that show the changes. With over 8 years as a GIS and Remote Sensing Analyst and 2+ years as a Machine Learning professional, I can take your company's spatial data analytics to the next level by incorporating machine learning to your workflows using PyTorch and Fast.ai. The work I do is 100% original and high quality and can be implemented in ArcGIS Pro, Google Colab or Google Earth Engine to take advantage of the petabyte scale satellite images hosted by google.
What's included
| Service Tiers |
Starter
$200
|
Standard
$400
|
Advanced
$600
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 20 days |
Number of Revisions | 1 | 2 | 4 |
Number of Model Variations | 1 | 2 | 4 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 2 | 4 | 6 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | |||
Source Code | - |
Frequently asked questions
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RG
Raj G.
Dec 31, 2025
Teach me how to use Claude to REVISE code properly
AC
Alexander C.
Dec 23, 2024
Google Cloud + Gemini AI API consultant to debug implementation
Very solid communication, work, knowledge and responsiveness
CB
Carolina B.
Aug 20, 2024
Find_beginning Time of formation and plot it
Excellent work and extremely proficient in anything python.
CR
Cyrus R.
Jul 10, 2024
Back-End Development for Generative AI Project
One of the best people I've worked with in any discipline and any capacity. John is the ultimate team player and is so knowledgable. He single-handedly set up all our cloud environments and built our backend data structures to scale into any use case we need. He's a superhero
JN
Janice N.
Feb 23, 2024
30 minute consultation
About John
Expert Vetted AI Engineer: Creating AI, GIS, and Cloud Solutions
100%
Job Success
Nairobi, Kenya - 4:58 am local time
🚀 What I Do:
- Design agentic workflows with structured I/O, tool use, and model fallback (OpenAI + Anthropic)
- Standardize data access behind an MCP tools layer (e.g., sales, advertising, content, keywords)
- Build FastAPI services optimized for LLMs (typed schemas, metrics filtering, slim payloads)
- Implement RAG/GraphRAG (vector search + graph relationships) to get from “what” to “why”
- Ship containerized ETL pipelines (idempotent, incremental) from services/APIs
- Deliver chat/web experiences with conversation memory, summarization, and tracing
👉 Outcomes You Can Expect
- Faster time‑to‑insight with traceable evidence and consistent schemas
- 30–90% payload reduction via metric filtering → lower latency and cost
- Safe, routine deploys with CI/CD; autoscaling and health probes in production
- A standardized tools layer that accelerates future features and integrations
🌐 Core Technologies
- AI/Agents: PydanticAI; OpenAI/Anthropic; RAG/GraphRAG
- Backend: Python, FastAPI, Pydantic, AsyncIO, httpx
- Data: MongoDB (motor, pooling, indexing, projection‑level filtering), vector search, knowledge graphs
- Ops: Docker, managed Kubernetes (HPA, liveness/readiness probes), CI/CD, secrets
- Reliability/Obs: rate limits, retries with backoff, token budgets, structured logs, tracing
📊 Typical Engagements
- Discovery & Architecture (1–2 weeks): use cases, ROI, target architecture, data contracts
- Build & Integrate (4–8 weeks): agents, MCP tools, services, RAG, observability
- Production Hardening (2–3 weeks): autoscaling, probes, CI/CD, SLOs/runbooks
- Managed Support (ongoing): reliability, cost tuning, knowledge/tool expansion
📊 Why Clients Hire Me
- End‑to‑end ownership: from requirements to production
- Strong defaults: small payloads, typed schemas, budget guardrails
- Clean contracts that make AI predictable and maintainable
👉 Share your goals and constraints—I’ll propose a workable plan with milestones, budgets, and measurable outcomes.
Steps for completing your project
After purchasing the project, send requirements so John can start the project.
Delivery time starts when John receives requirements from you.
John works on your project following the steps below.
Revisions may occur after the delivery date.
Gather the project requirements and Area of Study
Define the study area and understand the research questions that the analysis is supposed to answer. Identify the datasets that needs to be analyzed and the potential methods of analysis that can be applied.
Create Outline of the Analysis and the Training Classes
Generate the training classes that needs to be fed into a machine learning model as shapefiles or geojson. Deliver the classes together with outline of the upcoming analysis steps.