Ze (@fenghuaize) 's Twitter Profile
Ze

@fenghuaize

SNU
\\Ryu Lab
\\We believe in Science

ID: 1487006354300862464

calendar_today28-01-2022 10:15:34

74 Tweet

47 Followers

111 Following

RyuLab@SNU (@ryuyr77) 's Twitter Profile Photo

Hot off the press! New lab paper by Ryoungseob Kwon 🎉 Counted 1.2 M trees in a city with multimodal data & deep learning. Species detection done from vehicle and #CitizenScience Seoul National University CALS IRIS@UNIST CitizenScience.Gov @EcoVisionETH @JanDirkWegner1 Martin Brandt sciencedirect.com/science/articl…

Will Marshall (@will4planet) 's Twitter Profile Photo

Super excited to announce Planetary Variables are now available on our Sentinel Hub platform! This enables self serve access to these higher level products, helping unlock the power of Earth observation data for our users. Nice work team 👏investors.planet.com/news/news-deta…

Super excited to announce Planetary Variables are now available on our <a href="/sentinel_hub/">Sentinel Hub</a> platform!

This enables self serve access to these higher level products, helping unlock the power of Earth observation data for our users. Nice work team 👏investors.planet.com/news/news-deta…
NASA Acres (@acresprogram) 's Twitter Profile Photo

Yuchi Ma, of Stanford University, led a “Fine & DANNdy” talk about his group’s proposed method to improve subfield-level crop yield mapping. The method VAE-QDANN, uses a Variational AutoEncoder and quantile regression framework to filter outlier samples and improve accuracy. #AGU23

Yuchi Ma, of <a href="/Stanford/">Stanford University</a>, led a “Fine &amp; DANNdy” talk about his group’s proposed method to improve subfield-level crop yield mapping. The method VAE-QDANN, uses a Variational AutoEncoder and quantile regression framework to filter outlier samples and improve accuracy. #AGU23
Hannah Kerner (@hannah_kerner) 's Twitter Profile Photo

We need #machinelearning for satellite data ("SatML") to address planetary-scale challenges 🌍 but mainstream ML is ill-suited to SatML data & contexts. It's time we recognize satellite data as a distinct modality for ML research to unlock its potential 🦾arxiv.org/abs/2402.01444

We need #machinelearning for satellite data ("SatML") to address planetary-scale challenges 🌍 but mainstream ML is ill-suited to SatML data &amp; contexts. It's time we recognize satellite data as a distinct modality for ML research to unlock its potential 🦾arxiv.org/abs/2402.01444
RyuLab@SNU (@ryuyr77) 's Twitter Profile Photo

Hot off the press! New lab #RSE paper by Liang Wan 🎉Developed #Sentinel2 #Landsat 🛰️leaf Chl maps by correcting canopy structure effects 🌿🥬🍃 Benjamin Dechant Jeongho Lee Zilong Zhong Ze Seoul National University CALS sciencedirect.com/science/articl… Huge thanks to NEON for

RyuLab@SNU (@ryuyr77) 's Twitter Profile Photo

New lab paper by Liang Wan 🥳🎉 Developed LAI 🍀 algorithm for #Sentinel2 🛰️ Corrected for canopy structure, biochemistry, and soil background effects. Tested comprehensively in space and time With Yanghui Kang Benjamin Dechant Yorum Hwang Ze Sungchan Jeong

RyuLab@SNU (@ryuyr77) 's Twitter Profile Photo

📢Hiring! Two postdocs for global land carbon sinks Seoul National University 🌎🛰️ Will be fun with global collabo thru mobility funding for @BerkeleyBiomet Philippe.ciais Pierre Gentine Trevor Keenan Sandy Harrison Josep Penuelas Colin Prentice, Joe Berry, Jin Wu Please spread this

LEMONTREE (@lemontree_uofr) 's Twitter Profile Photo

Jeong et al's 2nd paper in Remote Sensing of Environment reveals a persistent global greening trend over the last 4 decades using refined NDVI and NIRv datasets. This contrasts with previous studies showing a decline in global greening. sciencedirect.com/science/articl… RyuLab@SNU

Jeong et al's 2nd paper in Remote Sensing of Environment reveals a persistent global greening trend over the last 4 decades using refined NDVI and NIRv datasets. This contrasts with previous studies showing a decline in global greening. sciencedirect.com/science/articl… <a href="/ryuyr77/">RyuLab@SNU</a>