BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//pretalx.earthmonitor.org//global-workshop-2026//CEMWWL BEGIN:VTIMEZONE TZID:Europe/Amsterdam BEGIN:STANDARD DTSTART:20001029T030000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:CET TZOFFSETFROM:+0200 TZOFFSETTO:+0100 END:STANDARD BEGIN:DAYLIGHT DTSTART:20000326T020000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:CEST TZOFFSETFROM:+0100 TZOFFSETTO:+0200 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:pretalx-global-workshop-2026-A98WPB@pretalx.earthmonitor.org DTSTART;TZID=Europe/Amsterdam:20261008T190000 DTEND;TZID=Europe/Amsterdam:20261008T191500 DESCRIPTION:Time series and spatial modeling are commonly used to generate cloud- and gap-free satellite imagery. Most existing approaches reconstruc t the entire dataset using advanced models\, which requires high computati onal resources and time. In this study\, we introduce a new\, computationa lly efficient pipeline to reconstruct monthly Landsat data without gaps or clouds. The pipeline includes four levels of gap filling. In the first st ep\, we apply a clean mask to biweekly Landsat data and create a 7-image w eighted window spanning the current and neighbouring months. For each band and month across the 28-year period\, we generate 25th and 75th percentil e thresholds and calculate a weighted median\, giving 50% weight to the cu rrent month and 25% to neighboring months\, using only values within the 2 5th–75th percentiles. In the second step\, remaining gaps are filled usi ng an annual land cover classification derived from the GLAD dataset and L andsat data from up to ten previous years\, restricted to pixels in the sa me land cover class. The third step fills small gaps of up to 2×2 pixels using a 4×4 averaging kernel. These steps fill approximately 40–60% of land pixels depending on tile location. Finally\, a pretrained temporal mo del is applied to fill the remaining gaps. We tested this pipeline on a CP U server with 96 threads and 1 TB RAM. Each tile can be processed in under 2000 seconds. Parallelization across tiles and bands enables global proce ssing in under six weeks\, significantly reducing the computational time c ompared to full dataset reconstruction\, which would take approximately si x months. The resulting dataset provides clean\, gap- and cloud-free month ly Landsat imagery suitable for a variety of research applications. Limita tions remain\, mostly related to input/output operations\, and future work could apply embedding models to reduce dataset size and produce abstract representations for faster access. DTSTAMP:20260624T065248Z LOCATION:Room 18 SUMMARY:A Multi-Layer Gap-Filling Pipeline for Continuous Monthly Landsat D ata (1997–2025) - Sajed URL:https://pretalx.earthmonitor.org/global-workshop-2026/talk/A98WPB/ END:VEVENT BEGIN:VEVENT UID:pretalx-global-workshop-2026-YKFVG9@pretalx.earthmonitor.org DTSTART;TZID=Europe/Amsterdam:20261008T164500 DTEND;TZID=Europe/Amsterdam:20261008T173000 DESCRIPTION:One of the most significant deliverables of the OEMC project ar e global\, cloud-less Landsat monthly time series from 2000–2025 at 30 m resolution. The Landsat global mosaics (V1) are explained in detail in Co nsoli et al. (2025\; https://peerj.com/articles/18585/). The Landsat V2 is at the order of magnitude more ambitious aiming at monthly products in 16 bit format and will significantly less artifacts. The pipeline uses a fou r-step process for improved quality\, including gap-filling using spatial and temporal neighbours\, data fusion and final gap filling using global m odels. The results of cross-validation show improvements in accuracy in co nsistency. Major project challenges include needing 1PB of storage and sec uring post-2025 commercial services. Landsat V2 can also be used to derive embeddings for 2000-2025. DTSTAMP:20260624T065248Z LOCATION:Rooms 12+14 SUMMARY:Landsat monthly cloud-free complete consistent mosaics 2000-2025 - Tom Hengl (OpenGeoHub)\, Sajed URL:https://pretalx.earthmonitor.org/global-workshop-2026/talk/YKFVG9/ END:VEVENT END:VCALENDAR