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    Protecting a family's lifetime savings from land encroachment, one plot at a timeNew

    Sharing what we're building at ShieldNex and why it matters, in case anyone here is working on adjacent problems.In India, a large share of household wealth sits in a single plot of land. For farmers, small business owners, and especially NRIs living abroad, that plot is often the outcome of decades of savings. It is also wide open to slow, quiet encroachment — a boundary wall creeping over, a shed going up on a corner, a track cut through a field. The owner usually finds out long after the change is on the ground, and the legal fight that follows can drag on for years. Families lose the land, the money, or both.We built ShieldNex to close that gap.Every plot is monitored on a fixed cadence using trusted satellite imagery. Our AI change-detection engine compares images over time and flags new construction, boundary shifts, and land-use changes. The owner gets an alert within 72 hours of a detected change, along with a geo-referenced evidence pack designed to stand up as an electronic record under India's IT Act 2000 and the Bharatiya Sakshya Adhiniyam 2023. Everything is sealed behind a SHA-256 chain of custody, so no single party — us included — can alter evidence without it being detectable.The point is that a landowner sitting in Dubai, Toronto, or the next district over gets to see their plot the way a farmer standing on it would, but continuously, and with proof.The outcome we care about is simple: a family's lifetime savings stays with the family. A grandmother's plot doesn't quietly turn into somebody else's driveway. An NRI doesn't come back from abroad to find a stranger's building on their land.Would love to hear from anyone in this community working on land protection, change detection for legal use, or evidence pipelines in emerging markets. Happy to compare notes on cadence, cloud cover, and chain-of-custody design.— Team ShieldNex shieldnex.in

    Community TeamPlaneteer 🌎

    Introduce Regional/Latitude-Gated Treatment of Snow Pixels during Monthly Basemap CompositingNew

     The global monthly mosaic generation pipeline currently ranks and selects imagery scenes based on data validity metrics derived from the Usable Data Mask (UDM2.1). While cloud pixels are properly identified as invalid and masked, snow pixels are treated as valid surface data.In equatorial regions—such as Singapore, where snow is a physical impossibility—the UDM2.1 classifier systematically misclassifies highly reflective, optically thick cloud patches as snow. Because the mosaic scene-ranking algorithm treats snow as valid, a cloudy scene containing these misclassified "snow" patches can mistakenly outrank a cleaner, less-cloudy alternative scene. This results in corrupted, saturated surface reflectance data permanently propagating into the final monthly composite product with no warning flags for downstream consumers.Proposed SolutionImplement an algorithmic check directly within the mosaic generation and scene-ranking pipeline to account for regional geographic realities without requiring a full retraining of the underlying UDM2.1 core classifier: Latitude-Gated Rule: When processing imagery or generating mosaics for Areas of Interest (AOIs) within the tropical belt, the scene-ranking pipeline must treat any pixel flagged as suspect or cloud-equivalent. Scene Re-ranking: Scenes with high "snow" percentages in these restricted tropical zones should have their quality scores penalized identically to cloud cover. This ensures the pipeline selects a truly clear alternative scene during compositing rather than a cloudy scene mislabeled as snow.