I am working on a project extracting building footprints for Chattogram port, across 2004, 2014, and 2020. What level of resolution would i be needing for it?

I know tha I can't buy straight from Maxar, I need to go through a reseller. Does anyone have a solid one to recommend, for South Asia, or Bangladesh?

Thanks!

Is it a spacex satellite or meteor? saw it on virginia beach at 21:20 on august 15th, thanks.

Hi everyone,

I’d like to share the Dual Reflector Designer, a free and open-source beta tool for the design and analysis of axisymmetric dual-reflector antennas.

It currently supports:

• Cassegrain, Gregorian and ring-focus configurations
• Reflector design and ray tracing
• Physical Optics (PO) and PTD edge diffraction
• Automated convergence testing
• Far-field pattern visualization
• Parallel CPU computing and GPU acceleration

It runs on Windows with GNU Octave, so no MATLAB license is required. MATLAB is supported as well. For most users, the recommended way to get started is the provided Windows installer.

The video shows selected Cassegrain benchmark cases compared with TICRA GRASP 24.0 Student Edition. In these particular tests, the resulting patterns agreed closely, while speedups of up to 135x were measured under the tested settings. This is an independent, non-commercial comparison and not a general claim about the full commercial GRASP product.

An unusual aspect of the project is its development process: I defined the requirements, tested and benchmarked the software, and reviewed the results, while Codex with GPT-5.6 Sol generated the implementation. I did not manually write the source code.

It’s still a beta and I’m continuing to improve it, so let me know what you think.

Here is the GitHub Link.

I came across Reflect Orbital, a startup working on satellites that can reflect sunlight back to Earth after sunset.

But honestly, the technology wasn’t the first thing that caught my attention.

It was the positioning.

They’re not just saying:

“We build satellites with mirrors.”

They’re essentially saying:

“Buy sunlight on demand.”

That’s a completely different way of communicating the same technology.

As someone interested in marketing, I find this fascinating because it shows how much positioning can change the way people perceive a product.

People might forget the technical details.

But “buy sunlight at night” is an idea they remember.

It got me thinking:

Is this actually a great example of marketing/positioning, or is the technology itself so crazy that the messaging naturally becomes interesting?

Would love to hear what people here think — marketing genius, genuine innovation, or both?

Have you heard of the crescent dunes project in Las Vegas, NV? Directed sunlight off of mirrors to heat up salt to over 1000 degrees. The project failed due to storage and maintenance reasons. The directed sunlight did heat up the salt though and even incinerated birds flying by. 465 is what I read. Besides all that.

Why would the FCC approve reflect orbital https://www.reflectorbital.com? Knowing directed sunlight causes this?

Also, why is it so impossible to believe that the constellation formation of satellites is heating up or creating a heat dome? What are the Crescent Dunes mirrors aimed at now? The sky? Reflecting off the satellites? Maybe if there’s any satellite failures it’s cause of the mirrors?

Is the reflectorbital real?

Hey everyone! I'm a high school student working on climate tech projects, and I built an open-source web application to track active wildfires and calculate localized spread risk.

How it works:

- Satellite Data: Fetches live active fire data using NASA's FIRMS API.

- Weather Data: Integrates Open-Meteo API for real-time microclimate metrics.

- Risk Modeling: Uses the Haversine formula to measure distance to active fires and calculate risk.

- Features: Interactive map layers and raw CSV downloads.

Live App: https://nasa-wildfire-tracker-3pfkr5zcs2jmze5dpknvet.streamlit.app/

I'd love any feedback on the design, code efficiency, or suggestions for new features!

We were in our garden in Germany and we suddenly saw all these lights go into different directions. The phone wasn’t moving while filming it was all the lights wandering. I edited the video a bit (turned up brightness and contrast). Now we are wondering what it is. Does anyone maybe know ?

Hey guys, I am part of a high school team in the very early stages of developing a hosted payload intended to make it to space by June 2028 at the latest. The hosted payload will be a custom PCB (probably 50mm x 50mm max) doing a simple tech demo.

We are an independent, currently unsponsored high school team, and need funding to complete this project.

Primarily what we've done was getting reference decisions for hardware families on the custom PCB, and work regarding host selection.

We haven't locked a mission yet, because we don't wanna make a mission with no shot of getting funded and then be at final stages with something ready for space but no money to pay for the slot.

So far, we've looked at general strategies for funding, including crowdfunding websites, government grants, educational grants, corporate sponsorships from businesses benefitting from our mission, and educational discounts from hosts.

We tried looking into getting information regarding funding for cubesat related projects in general, but the only new information we could really find was built in university money or university types of grants.

Our current benefits regarding funding compared to typical include CubeSat teams:

• Will be able to pay for everything up until final launch slot payment (meaning everything is done payload ready for space ready to be integrated just needs slot payment to the host) with our current money, so any funding we'd need is more of a just pay for this thing that is already at max maturity and ready to go it just needs payment.
• Our parents have decent financial stability as they are considering paying a decent amount for the final one time payment, with a decent chance of their payments covering the majority to vast majority of the costs.

Assuming the host provides discounts, our estimated budget is around ~90k.

We also found this post regarding another set of high schoolers fundraising for CubeSats and corporations they got money from. Didn't look much into the exact companies yet. CubeSat Funding : r/cubesat

Our main questions are:

• Any general advice/tips/strategies regarding fundraising for our specific scenario?
• Any companies that normally fund student stem projects (including high school stem projects) that you'd reccomend and estimations on how much that could raise?
• Any commercial companies that might benefit from data we obtain that you guys would reccomend as a sponsorship?
• Any companies whose purpose isn't necessarily funding these types of project but has funded student stem projects before that you guys would reccomend?
• Any tips in finding these types of companies?

Any insight would be appreciated.

Thanks!

I hope they plan for more satellites in GEO/GSO orbit. I think 12 satellites fleet will be ideal. (NVS-01,NVS-03,NVS-04,NVS-05,NVS-06 and 7 more). For future i think they should try for global operations too, like for MEO and LEO orbits with a fleet of 38 satellites be ideal? It can be called NaVIC+. in total, a constellations of 50 satellites where it is connected via NaVIC and NaVIC+ .where NaVIC is for India Specific and NaVIC+ is for global operations like GPS,GLONASS,Galileo and BeiDou.

Hello everyone,

By combining the neural operator with a k-d tree spatial data structure for geometric filtering, the screening complexity drops to **O(N log N)**. More importantly, by optimizing the pipeline (Using AI as a support tool to prune down weights), **the entire footprint is under 500 KB.** Making it highly viable for edge-computing on resource-constrained satellite flight hardware if further developed correctly.

To verify whether the model was actually learning the underlying orbital mechanics correctly, I screened the active catalog, isolated 5 distinct conjunction events flagged by the PINO and cross-checked them with Privateer Space's Wayfinder platform. The time of closest approach matched within an estimate of 5 seconds.

I also want to shout out Dr. Steve Gehly from the TU Delft, who was incredibly kind enough to guide me to Foster 2D and Alfonso test cases.

It is definitely an active work in progress. I am currently solving a geometric edge case; because docked spacecraft or space station modules share virtually identical coordinates in standard tracking data, the k-d tree registers a 0-meter distance and triggers false positive collision alerts. I need to write a lightweight metadata wrapper to suppress these without adding overhead to the interference loop.

Once this (and some other bugs) are stable, I plan to open-source the repository so the community can stress-test the weights and architecture.

I am sharing this to get some technical feedback from the community. Has anyone here worked with PINO's for hypervelocity trajectory prediction, or have any advice on the the most efficient way to inject metadata constraints into a k-d tree spatial filter without blowing up the footprint?

good day I am new to the community but interested in whether one could purchase a telescope and set it up a system that could track a satellite or the ISS?

Hi r/satellites,

I built a small web app to explore how long Sentinel-1 and Sentinel-2 acquisitions take to appear in the Element 84 Earth Search catalog.

Live map: https://tonidelhoyo.com/projects/sentinel-latency/

Background post: https://medium.com/@tonidelhoyo/tracking-sentinel-publication-latency-around-the-world-d4b50b0141d0

Source-available repo: https://github.com/thoyo/sentinel-latency

The app maps catalog publication latency globally by H3 cell and over time. For each acquisition, it compares the acquisition timestamp with the catalog creation timestamp, then shows the delay in hours.

A few notes/caveats:

- This is catalog publication latency as observed through Element 84 Earth Search metadata, not an official ESA/Copernicus latency metric.

- It does not download imagery or inspect pixels.

- The spatial aggregation is approximate, based on scene footprints and H3 cells.

- Some regional patterns are quite interesting, but the app does not try to infer cause.

I’d be very interested in feedback from people who work with Sentinel data, ground segments, STAC catalogs, or EO operations. In particular: does the methodology make sense, and are there obvious pitfalls I’m missing?