A project-first program in wildfire, forestry, and SAR analytics, taught by the team behind Lillie Earth Intelligence's operational satellite systems. You finish with four portfolio projects, three public write-ups, and a deployed monitoring service.
The program is built so a motivated beginner and a working scientist both reach the same professional bar, without either one wasting time.
Start at Phase 0. You will set up a professional Python and geospatial toolkit, learn how Earth observation actually works, and understand raster, vector, and coordinate systems before any modeling. No prior GIS or machine learning required.
Skip straight to Phase I. If you can already build a cloud-native data cube, you spend your time on SAR depth, foundation models, and production systems, not on fundamentals you have. Not sure? Take the 2-minute placement diagnostic.
Every module commits into one public repository with tests and continuous integration, so the portfolio writes itself as you go.
Burn-severity mapping, forest structure and carbon loss, a foundation-model benchmark, and a near-real-time wildfire service.
Clear, honest results notes on SAR burned-area detection, deep learning versus foundation models, and an end-to-end pipeline.
A containerized, scheduled "firewatch" that turns a hotspot into a damage report, running on cloud infrastructure with a live demo map.
A single study area runs through the whole course: one recent large wildfire and one tropical-forest tile, so every skill compounds on familiar ground.
Read the course modules See the week-by-week tracker
The target list includes Planet, ICEYE, Capella, Overstory, Pano AI, Pachama, Sylvera, CTrees, Development Seed, and NASA JPL. Week 12 is a structured, targeted job search, not an afterthought.
Speckle statistics, terrain correction, polarization, and change detection, not just "we used Sentinel-1." This is what SAR vendors screen for.
Fine-tune and benchmark modern geospatial foundation models against your own U-Net, with honest low-label and cost analysis.
Docker, orchestration, object storage, tiling, and CI. The gap between "scientist" and "ML engineer" on real job descriptions.
Spatial cross-validation, area estimates with confidence intervals, and uncertainty. You learn to report numbers you can defend.
Hands-on with the newest sensors and missions, including NISAR, so your portfolio shows skills most applicants do not have yet.
Every module produces a public, tested artifact. You are not collecting certificates, you are building evidence.
Every part of the experience is built to keep you moving and to prove your skills, not just certify that you showed up.
A course-aware assistant searches all sixteen modules and points you to the exact lesson that answers your question. Full conversational tutoring and code review against a mentor's rubric are Coming soon. Try it →
Each lab lands in your GitHub repository and is checked automatically by a test suite and continuous integration. You get instant, objective feedback and a green badge that proves the work actually runs.
Finish and earn a shareable certificate with a unique verification link, ready to add to your LinkedIn profile. It points back to the public portfolio you built, so it means something to a hiring manager. Verify a certificate →
Learn alongside peers in a private community with accountability groups, office hours, and a showcase of learner projects. Community is the single biggest reason people finish a self-paced course.
Open a one-click cloud environment with the whole geospatial stack ready to run. No fighting a GDAL install, no lost weekend, just code from the first lesson.
The curriculum will be versioned with a public changelog and updated as satellite missions and models change, so what you learn matches what employers use this year, not three years ago.
Lillie Earth Intelligence runs production satellite monitoring systems across Nigeria: LillieWatch for energy-infrastructure security and LillieOre for illegal-mining detection. The course teaches the exact stack, rigor, and honesty we use in the field, from someone shipping it, not summarizing it.
Foundations is free forever. The full program is $199 for lifetime access, and founding-cohort places for the first 50 learners are $99. Regional pricing keeps it reachable everywhere. Paid enrollment opens with the founding cohort, so join the waitlist to claim a founding place.
Founding-cohort price for the first 50 learners. Lifetime access. Then $199. Regional pricing applies.
Serious training should be reachable wherever you are, and risk-free to begin.
Prices adjust to local purchasing power, so the program stays reachable in emerging markets while staying premium where budgets are larger. In Nigeria and similar markets the full program is about $59 to $79, with a founding price near $39, applied automatically at checkout.
Spread the cost across monthly payments if that suits you better, with no penalty for choosing a plan over paying upfront.
Start with confidence. When paid enrollment opens, if the opening modules are not right for you within 14 days, ask for a full refund, no questions asked.
Readable transcripts, strong color contrast, and full keyboard navigation. Captioned video is Coming soon.
The program is new, so rather than borrowed testimonials you get a founding-cohort place: the $99 founding price, direct access to the team, and your work featured in the first learner-project showcase. Real outcomes, published honestly as they come in.
We also set aside ten need-based scholarships, full free seats, for students and early-career applicants in Nigeria. If cost is the only thing standing between you and the program, tell us.
Join the waitlist and we will send you Phase 0 first, then early access to the full program.
Join the waitlist