Andres Aguilar Ariza

Look what we have here !! my first paper as a PhD student, a method to efficiently predict individual plant weight using #UAV-based data, #MachineLearning and #ComputerVision approaches. #RemoteSensing #plantphenotyping. nature.com/articles/s4159…

Nuestro equipo presentó avances del primer Sistema Nacional de Información de Suelos de Honduras a la Secretaría de Agricultura y Ganadería. Junto a SAG y DICTA, avanzamos hacia una alianza para fortalecer la gestión de suelos con datos y ciencia. 🌱🤝 🔎 allbiociat.org/4qrJSYu

[Updated! 461-page PDF eBook] Physics-based Deep Learning: arxiv.org/abs/2109.05237 + Learn more at: physicsbaseddeeplearning.org/intro.html —————— #AI #MachineLearning #ML #DataScience #NeuralNetworks #Algorithms #Mathematics #DataScientist

We are thrilled to announce that Google’s Satellite Embedding dataset, powered by @GoogleDeepMind's AlphaEarth Foundations model, has been updated for 2025. This additional year of coverage now unlocks the ability to look back, compare, and detect change across the planet with unprecedented clarity. Learn more here ➡️ goo.gle/3NdIxWn Part of Google's Earth AI, the new data represents the state of the planet throughout 2025, distilling petabytes of multi-sensor data into a 64-dimensional embedding for every 10 meter pixel. What’s new in this update? 🧵👇 - 🌍 2025 Data: The state of the planet throughout 2025 is now available on the Earth Engine Data Catalog and Google Cloud Storage. - 🔬 Unprecedented Change Detection: Because these embeddings capture subtle spectral, spatial and temporal signatures, they make it easy to spot significant year-over-year changes without the heavy lifting of raw image processing. - 💚 Long-term Commitment: We are formalizing our commitment to the ongoing production of these annual layers to support your operational workflows. Since we first launched the Satellite Embedding dataset, we’ve been inspired by how our community is putting this data to work. Applications are ranging from ecosystem mapping and agricultural crop-typing to carbon stock prediction. We can’t wait to see what you do next. #EarthEngine #GeoAI #DeepMind

All of my Deep RL course lecture videos from Spring 2025 are now online! 🥳 Youtube playlist: youtube.com/watch?v=EvHRQh…

Even though I’m a much better Python than JavaScript developer, with AI assistance, I’ve been writing a lot of JavaScript code recently. AI-assisted coding, including vibe coding, is making specific programming languages less important, even though learning one is still helpful to make sure you understand the key concepts. This is helping many developers write code in languages we’re not familiar with, which lets us get code working in many more contexts! My background is in machine learning engineering and back-end development, but AI-assisted coding is making it easy for me to build front-end systems (the part of a website or app that users interact with) using JavaScript (JS) or TypeScript (TS), languages that I am weak in. Generative AI is making syntax less important, so we can all simultaneously be Python, JS, TS, C++, Java, and even Cobol developers. Perhaps one day, instead of being “Python developers" or “C++ developers,” many more of us will just be “developers”! But understanding the concepts behind different languages is still important. That’s why learning at least one language like Python still offers a great foundation for prompting LLMs to generate code in Python and other languages. If you move from one programming language to another that carries out similar tasks but with different syntax — say, from JS to TS, or C++ to Java, or Rust to Go — once you’ve learned the first set of concepts, you’ll know a lot of the concepts needed to prompt an LLM to code in the second language. (Although TensorFlow and PyTorch are not programming languages, learning the concepts of deep learning behind TensorFlow will also make it much easier to get an LLM to write PyTorch code for you, and vice versa!) In addition, you’ll be able to understand much of the generated code (perhaps with a little LLM assistance). Different programming languages reflect different views of how to organize computation, and understanding the concepts is still important. For example, someone who does not understand arrays, dictionaries, caches, and memory will be less effective at getting an LLM to write code in most languages. Similarly, a Python developer who moves toward doing more front-end programming with JS would benefit from learning the concepts behind front-end systems. For example, if you want an LLM to build a front end using the React framework, it will benefit you to understand how React breaks front ends into reusable UI components, and how it updates the DOM data structure that determines what web pages look like. This lets you prompt the LLM much more precisely, and helps you understand how to fix issues if something goes wrong. Similarly, if you want an LLM to help you write code in CUDA or ROCm, it helps to understand how GPUs organize compute and memory. Just as people who are fluent in multiple human languages can communicate more easily with other people, LLMs are making it easier for developers to build systems in multiple contexts. If you haven’t already done so, I encourage you to try having an LLM write some code in a language you’d like to learn but perhaps haven’t yet gotten around to, and see if it helps you get some new applications to work. [Original text: deeplearning.ai/the-batch/issu… ]

🚀 Excited to share our latest paper, "Earth System Data Cubes: Avenues for Advancing Earth System Research", just published in @EnvDataScience! 🌍📊 📜 doi.org/10.1017/eds.20…

🚨 Our new paper introduces #FloodCast, a scalable AI-driven system for large-scale flood modeling using satellite data. 🌊🌍 Paper: sciencedirect.com/science/articl… Code: github.com/HydroPML/Flood… #FloodModeling #AI #RemoteSensing #Hydrodynamics #ClimateTech

Excited to dive into #MachineLearning for #EarthObservation? 🌱🌐 Check out this free MOOC: "Introduction to Machine Learning for Earth Observation" being part of #ML4Earth: eo-college.org/courses/introd… link to project: ml4earth.de

Hoy y mañana tendremos el honor de recibir en nuestro campus de Palmira a Laura Suazo, ministra de Agricultura y Ganadería de Honduras; a Mario López, subdirector de la Dirección de Ciencia y Tecnología Agropecuaria; y a Any Reyes, asesora en Mecanismos Financieros de la SAG.

Current LLMs are trained on text data that would take 20,000 years for a human to read. And still, they haven't learned that if A is the same as B, then B is the same as A. Humans get a lot smarter than that with comparatively little training data. Even corvids, parrots, dogs, and octopuses get smarter than that very, very quickly, with only 2 billion neurons and a few trillion "parameters."

Review paper out😊 Safonova et al. "Ten deep learning techniques to address small data problems with remote sensing" Observational data is often insufficient for key environmental issues. We tackled this. authors.elsevier.com/sd/article/S15… @zalf_leibniz @ghazaryangohar

Learn about Cloud Score+, a new cloud and cloud shadow detection product that can help you improve your #EarthEngine workflows for clear compositing and beyond! ☁️☁️☁️medium.com/google-earth/a…

Public funds being swallowed up by scientific journals with dubious articles english.elpais.com/science-tech/2… via @elpaisinenglish

Summer 2023 was the hottest summer in NASA’s record, continuing a trend of long-term warming caused by human activity. All three summer months broke heat records: go.nasa.gov/48cqdmn

The first most comprehensive textbook for #EarthEngine "Cloud-Based Remote Sensing with Google Earth Engine" now has many video #tutorials by authors, including the chapter I contributed with Dr Sam Bowers. Hope you find it useful! youtu.be/sKvLNjFAriE?si…

July 2023 was the hottest month on record, according to our global temperature analysis. Overall, July was 0.43°F (0.24°C) warmer than any other July in @NASAEarth's record, and it's likely due to human activity. Details: go.nasa.gov/3OTWMh7

How hard is it to do science in a language that’s not your first language? We quantified the severity of #languagebarriers for non-native English speakers when reading/publishing papers and attending/presenting at international conferences. doi.org/10.1371/journa… 1/9

Would you like to learn how to process and interpret #SAR data for multiple applications? Checkout #GEE hands-on exercises for deforestation analysis 👇

We at @Google created FREE comprehensive training on Generative AI! Get ready to apply AI in your workplace. 👉 Intro to Generative AI 👉 Intro to Large Language Models 👉 Intro to Responsible AI 👉 Intro to Image Generation 👉 Encoder-Decoder 👉 Attention Mechanism 👉 Transformer Models and BERT Model 👉 Create Image Captioning Models 👉 Intro to Generative AI Studio Course Link: cloudskillsboost.google/paths/118 Blog: cloud.google.com/blog/topics/tr… #GenAI #MachineLearning #ArtificialIntelligence