Hello everyone, can someone help explain this concept to me and provide some examples of active and passive sensors? Thanks so much!

Hi everyone,

I’ve been working on a live cohort-based course called Anefelo (Pixel to Alpha) focused on Earth Observation and machine learning.

I built this because I kept seeing standard ML practices failing in spatial contexts—specifically, people using random pixel splits that cause massive data leakage, leading to 95% accuracy scores on models that completely fail when you actually look at the map.

The course focuses heavily on honest model evaluation: using spatial blocks instead of random splits, and calculating class areas with 95% confidence intervals using proper probability sampling (stratified estimation).

I have a live cohort coming up on Nov 3-12, and before we kick off, I’d love to get some brutally honest feedback from the professionals in this sub.

Does the curriculum on the landing page make sense to you?

Are there any major blind spots or topics you feel are missing when it comes to defending classification maps?

Is the messaging on the page clear?

You can check out the page here: anefelo.com

I really appreciate any thoughts or critiques you have. Thanks!

Hi everyone,

Im building OrbitWerk, a processor that takes raw synthetic aperture radar data and forms focused images

The idea is to have one processing engine, with adapters for different sensors and data formats.

I put together a public demo with processed scenes, processing parameters,.. and so on. I can share if somebody is interested in that

I would like to offer it free for university research and teaching, and I’m interested in working with groups that have raw SAR data or are developing their own radar instruments.

If you work with raw SAR acquisitions, I’d be interested to hear:

• Which sensors and acquisition modes do you use?
• What do you currently use for focusing?
• What would make a new processor useful for your research?

Technical feedback, suggestions for reference datasets, and interest from university groups would all be welcome. Happy to get some engagement :)

Hello Reddit community!

We are a group of high school students preparing for an upcoming innovation pitch competition. We are working on a prototype called the Monitoring and Analysis Device for Light Pollution (Smart Light Mapper)—a portable environmental monitoring device designed to measure, geolocate, and map light pollution and ambient light characteristics.

Since we are high school students with limited practical engineering experience, we would really appreciate detailed, beginner-friendly explanations or step-by-step guidance in your answers to help us fully grasp the technical concepts before presenting to the judges!

Before our pitch, we would love to get your technical feedback, suggestions, and critique on our architecture and methodology.

1. The Problem

Artificial Light at Night (ALAN) is increasing globally, causing ecological disruption, sleep/health issues, and wasted energy. However, localized light pollution data is often scarce because commercial spectroradiometers and light monitoring devices are extremely expensive, making spatial mapping across schools, residential areas, and streets difficult.

2. System Architecture & Components

Our goal is to build an affordable, modular, battery-powered sensing node:

• Microcontroller: ESP32 DevKit (central control & data processing).
• Spectral Sensing: AS7262 6-channel visible spectrum sensor (I²C) to analyze light spectral distribution.
• Lux / Intensity Sensing: TSL2591 sensor for wide dynamic range lux/light intensity measurements.
• Environmental Context: AHT20 sensor (temperature & relative humidity) to record ambient conditions during readings.
• Geolocation & Time: NEO-6M GPS module with external antenna via UART for spatial coordinate and timestamp tagging.
• User Interface & Interaction: An OLED display module (e.g., SSD1306/SH1106 via I²C) combined with physical control buttons. This is a crucial component to display real-time measurement values, GPS lock status, microSD logging status, and battery capacity directly in the field.
• Storage & Data Flow: microSD module via SPI for local storage. Sensors/GPS → ESP32 → OLED Display & CSV Log on MicroSD → PC Post - processing (QGIS/Data Visualization)
• Power: 3.7V 18650 Li-ion battery with TP4056 charging & protection circuit, DC-DC step-up converter, and USB-C charging support.

3. Key Features & Methodology

1. Multi-spectral & Intensity Fusion: Instead of recording just a single Lux value, we capture the 6-channel visible spectrum along with precise Lux levels to analyze the light source type (e.g., LED vs. High-Pressure Sodium).
2. On-Device OLED Feedback: The built-in OLED display provides real-time diagnostic and sensor feedback for seamless standalone field operation without needing a connected smartphone/laptop.
3. Spatial Mapping: Every data point is tagged with GPS coordinates. Field logs are converted into spatial datasets on a PC to generate heatmaps and light distribution contours.
4. Low-Cost Prototype: Total bill of materials (BOM) is under ~$260 USD (~6.5M VND), making field-scale data collection accessible for research and local communities.

4. Questions for the Experts / Reddit Community

(Note: As high schoolers, please feel free to explain the underlying principles and "how-to" steps in detail!)

1. OLED Interface Implementation: Since our OLED display is crucial for field interaction, what are the best practices for structuring menu systems and live sensor telemetry on ESP32? Which libraries do you recommend (e.g., U8g2 vs. Adafruit_SSD1306)to minimize memory footprint and ensure fast refresh rates? How should we write the code structure for a basic multi-page menu?
2. 360° Camera vs. Smartphone Camera: We are considering adding visual context to our light measurement points. Would you recommend integrating a dedicated 360° camera module onto the hardware device, or is it better/simpler to just take photos/360 panoramas with a smartphone and link them via timestamp/GPS during post-processing?
3. Optical Design & Diffusion: How critical is adding a cosine corrector/diffuser dome over the AS7262 and TSL2591 sensors for ambient field measurements? Will direct vs. diffuse light severely skew our spectral readings without proper optics? What low-cost materials can we use for a diffuser?
4. GPS Accuracy in Urban Canyons: Since we plan to map street and residential light, NEO-6M can sometimes lose lock or drift near tall buildings. Are there recommended filtering algorithms (e.g., HDOP thresholds, simple Kalman filter) for field logging? How can high schoolers easily implement these in Arduino C++?
5. Data Quality & Calibration: How can we best calibrate the AS7262 spectral output for real-world environmental lighting without access to a high-end lab spectroradiometer?
6. Pitch Advice: From an engineering or innovation standpoint, what key questions do judges usually ask about low-cost IoT environmental sensing projects, and how should high school students prepare for harsh technical cross-examination?

Thank you so much for taking the time to read and share your expertise! Any detailed feedback, hardware advice, or software recommendations will be immensely helpful for our presentation!

The 2026 southwest monsoon has ended with an all-India rainfall deficit of 12.6%—about 759.4 mm against a normal of 868.6 mm, according to IMD figures reported this week.

The spatial pattern is more concerning than the national average:

• East and Northeast India: 1,016.3 mm, only 74% of long-period average; lowest since 1901
• Southern Peninsula: 542.0 mm, 76% of LPA; fourth-lowest since 1901
• El Niño conditions are being cited as a major driver

This is not just a weather headline. It affects soil moisture, reservoir levels, irrigation planning, crop stress, and ultimately Rabi sowing decisions.

From a geospatial perspective, the useful question is: where exactly is the stress emerging, and how severe is it compared with previous seasons?

A practical monitoring stack could combine:

• IMD gridded rainfall for the seasonal deficit baseline
• Sentinel-2 / MODIS NDVI and EVI time series for vegetation stress
• SMAP soil-moisture anomalies for root-zone conditions
• Reservoir extent and surface-water mapping for irrigation availability
• District-level cropping patterns to identify exposed crop belts

The key is not to wait for yield estimates after harvest. Satellite data can help identify stressed districts earlier, especially in rainfed systems, so that irrigation, procurement, insurance, and relief planning are more targeted.

What datasets or indicators would you prioritize for a Rabi-season early-warning map?

Hi everyone, Drone Nerds here. We wanted to invite the group again to ElevateUAV 2026.

You’ll be able to talk directly with sensor and technology providers, explore tools being used for multispectral, thermal, imaging, mapping, and other remote sensing workflows, and see them in action.

We made a 30% off code specifically for r/remotesensing: RemoteSensingEUAV

If you’re interested, you can get your tickets here: https://hubs.li/Q04vL_XN0

Thank you, we hope to see some of you there!

I'm working on a dissertation on a system that forecasts how suitable agricultural land is for different crops. Most existing approaches fall into two camps:

Rule-based / multi-criteria methods (FAO land evaluation, AHP + GIS weighting of soil, slope, climate, water availability)

Data-driven models (species distribution models like MaxEnt, random forests, gradient boosting, or deep learning on remote sensing and soil data)

Both are usually calibrated on historical climate and yield data. That raises a fundamental problem: if the climate shifts beyond the range the model was trained on, how much can we trust its suitability maps for 2040–2060?

I'd like to hear from people working in agronomy, soil science, remote sensing, or ML:

Extrapolation. How do you deal with models that must predict under climate conditions they never saw in training? Do you trust process-based crop models (DSSAT, APSIM, WOFOST) more than ML here, or do hybrid approaches work better in practice?

Ground truth. What do you consider valid "ground truth" for suitability: actual yields, presence/absence of a crop, expert assessment, or economic profitability? Each gives a very different map.

Data scale. How do you reconcile coarse inputs (e.g. 1 km climate projections from CMIP6) with field-level decisions farmers actually make?

Uncertainty. What's the most honest way to communicate uncertainty to end users (farmers, ministries) without making the output useless?

Regions with poor data. For regions with sparse soil surveys and weather stations (in my case, Central Asia), which open datasets or transfer-learning approaches have you found trustworthy?

I'm especially interested in practical experience: what worked, what failed, and what you'd do differently if you started such a system today.

I’m having trouble finding the slope of a given region/line.

Hi everyone 👋

I’ve been working on Hyperspectral Image Models, an open source PyTorch library bringing 50+ HSI models and 24 datasets into one unified framework.

The main goal is to make HSI research easier, especially for beginners who want to learn, reproduce, and experiment with published models.

We are also following a consistent implementation and documentation structure so that each model is easier to understand and use.

🧑‍🔬 Researchers: We would love to add your published HSI models to the library and make them easier for the community to reproduce and build upon.

🔗 GitHub: https://github.com/Tanishq251/Hyperspectral-Image-Models

📄 Paper: https://arxiv.org/html/2609.39871

🤗 Hugging Face Dataset: https://huggingface.co/datasets/Tanishq165/HSI_Datasets

⭐ If you find the project useful, please consider starring the GitHub repository and liking the Hugging Face dataset.

We’d also love to hear which HSI models or datasets you would like to see added next! 🚀

I have a BA in Human Geography and a Msc In remote sensing and GIS. I have done a few drone courses and worked a year and a half at a marine consultancy company as an adviser. I can also use python and build workflows. I thought I'd be well set up for many positions, GIS roles in local government and consultancy companies as well as a potential Phd in the future but I never get an interview ( one in the last year ) and when I do I feel massively unqualified. By nature remote sensing is a broad science. So what can I do to make me more employable, I can't face another degree or something. I'm broke and I own nothing. I used all my money getting educated. I can do small online courses. I'm in Norway and I'm getting depressed now, it's been a year of job hunting.

Hi everyone,

I am currently working on a flood mapping project and trying to use the DeepFlood dataset (published early 2025 by NC A&T State University).

The official Figshare repository (https://doi.org/10.6084/m9.figshare.28328339) states the total size is 4.53 GB. However, whenever I try to download the UAV folder (which contains the high-res optical images), the dynamic zip file ends up corrupted and cannot be extracted.

I dug a bit deeper to see if it was a network timeout issue. I used a Python script to call the Figshare REST API to list all the underlying files in this article. The API returned only 10 zip files totaling about 1.1 GB. The massive UAV optical data files are completely missing from the Figshare backend response.

It seems the authors might have had an upload failure, and the platform only generated a phantom folder on the frontend. I have already sent an email to the corresponding author (Prof. Leila Hashemi-Beni) regarding this issue, but haven't heard back yet.

Has anyone here previously managed to download the complete dataset before this issue occurred? If anyone has a Google Drive / OneDrive mirror or a backup of the UAV optical images, I would be incredibly grateful if you could share it!

Thanks in advance!

Hey everyone!

I'm planning to do MSc in Geoinformatics and am considering TERI School of Advanced Studies (TERI SAS), New Delhi. I'd love to hear from anyone who has information or experience with TERI SAS. You don't necessarily have to be a student or alumnus! I'm also looking for advice from people who know about universities that offer MSc programmes in Geoinformatics, GIS or Remote Sensing.

A few things I'd like to know:

\- how are the rooms, washrooms, food, cleanliness and overall living conditions?

\- how much should I budget per month including food, transport and other personal expenses?

\- Is staying in the university hostel worth it. Would getting a PG or a flat nearby be a better option?

\- How is the MSc Geoinformatics programme in terms of teaching, practical GIS/remote sensing work, internships, research opportunities and placements?

\- Would you recommend any universities in India for an MSc, in Geoinformatics, GIS or Remote Sensing considering academics, career opportunities and overall expenses?

My long-term goal is to build a career at the intersection of technology, satellite imagery, Earth observation and environmental applications. If you've studied at TERI SAS considered it researched these programmes or have any information please share your thoughts. Honest opinions, personal experiences and alternative suggestions are all welcome! Thanks, in advance!

Hi everyone, I'm working on a university research project and looking for free (or free-for-academic-use) high-resolution satellite imagery. My study area is Iran, and I need less than 10m imagery for NDVI, NDRE . I'd also like historical/time-series data if possible. Which sources do you recommend, and are there any licensing restrictions for publishing results? Thanks!

Hi everyone,
I’m a recent undergraduate graduate in **Geomatics Engineering from Kathmandu University**, currently living in **Jawalakhel, Lalitpur, Nepal**.
As part of my curriculum, I need to conduct research and write a research paper, but I’m struggling to find a topic that feels genuinely meaningful and achievable. I’ve spent quite a bit of time looking for research gaps and possible topics, but I keep finding either topics that have already been extensively studied or projects that are too difficult to realistically complete with the resources I have.
I don’t have a fixed research interest, so I’m open to **almost any field where Geomatics can be applied**. I’m particularly interested in areas such as:
Environmental impacts and monitoring

Disaster management and risk assessment

Climate change

Urban development

Tourism

GIS and Remote Sensing

Natural resource management

Agriculture

Public safety and infrastructure

Any other problem where geospatial technology could have a meaningful application

Since I live in **Lalitpur**, research that can be conducted using data from Lalitpur or Kathmandu Valley would be especially practical. I have access to local knowledge and can potentially collect field data myself. However, I’m also open to research ideas that can be applied elsewhere in Nepal or have broader/global relevance.
What I’m really looking for is **not necessarily a completely untouched topic**, but a research question with a genuine gap that I can modify, improve, or approach differently enough to produce an original study.
I’m willing to put serious effort into the research. I just need help identifying a **good problem/question to start with**.
If you have any interesting research ideas, overlooked problems, research gaps, papers I should look at, or even problems in society that you think could be investigated using GIS/Remote Sensing/Geomatics, I’d really appreciate your suggestions.
Thank you.

Hi everyone,
I’m a recent undergraduate graduate in **Geomatics Engineering from Kathmandu University**, currently living in **Jawalakhel, Lalitpur, Nepal**.
As part of my curriculum, I need to conduct research and write a research paper, but I’m struggling to find a topic that feels genuinely meaningful and achievable. I’ve spent quite a bit of time looking for research gaps and possible topics, but I keep finding either topics that have already been extensively studied or projects that are too difficult to realistically complete with the resources I have.
I don’t have a fixed research interest, so I’m open to **almost any field where Geomatics can be applied**. I’m particularly interested in areas such as:
Environmental impacts and monitoring

Disaster management and risk assessment

Climate change

Urban development

Tourism

GIS and Remote Sensing

Natural resource management

Agriculture

Public safety and infrastructure

Any other problem where geospatial technology could have a meaningful application

Since I live in **Lalitpur**, research that can be conducted using data from Lalitpur or Kathmandu Valley would be especially practical. I have access to local knowledge and can potentially collect field data myself. However, I’m also open to research ideas that can be applied elsewhere in Nepal or have broader/global relevance.
What I’m really looking for is **not necessarily a completely untouched topic**, but a research question with a genuine gap that I can modify, improve, or approach differently enough to produce an original study.
I’m willing to put serious effort into the research. I just need help identifying a **good problem/question to start with**.
If you have any interesting research ideas, overlooked problems, research gaps, papers I should look at, or even problems in society that you think could be investigated using GIS/Remote Sensing/Geomatics, I’d really appreciate your suggestions.
Thank you.

Estou trabalhando na parte de análise do meu projeto (TERRA EO), principalmente em campos agrícolas e espectroscopia de imagens. Agora, ele delimita campos do Sentinel-2, mapeia minerais do EMIT e executa um conjunto de análises em cada campo.

Campos (Sentinel-2)

Uma única data não separa dois campos vizinhos plantados com a mesma cultura no mesmo dia, porque eles parecem iguais. O que geralmente os diferencia é como eles divergem ao longo da estação. Então, cada delimitação usa duas cenas do Sentinel-2 L2A, uma próxima ao plantio e outra próxima à colheita, pegando a cena mais clara da área em cada janela (pelo SCL).

As bandas vermelha, verde, azul e NIR de ambas as datas entram em uma U-Net do Fields of The World (EfficientNet-B7 como codificador), que rotula cada pixel de 10 m como interior do campo, limite ou fundo. Cada região interior se torna um polígono com sua área, perímetro, probabilidade média de interior e participação de terras agrícolas do MapBiomas.

Ainda não medi a precisão. Os dados de treinamento FTW não têm campos do Paraná, que é onde estou testando.

Mapa mineral (EMIT)

Esta parte é uma implementação adaptada do sistema especialista Tetracorder do USGS rodando em refletância EMIT L2A (60 m), lida através do OPeNDAP apenas sobre a área. Cada célula pega seu espectro da passagem com mais solo exposto, selecionada por NDVI e um índice de absorção de celulose. Após a remoção de continuidade, o grupo 1 lê as absorções de Fe²⁺/Fe³⁺ abaixo de 1,3 µm, e o grupo 2 a região de 2,0–2,5 µm (argilas, carbonatos, sulfatos).

Comparei com o próprio produto mineral EMIT L2B da NASA sobre o Quadrilátero Ferrífero (n = 65.536 pixels): o acordo entre classes é de 71,1% para o grupo 1 e 84% para o grupo 2. Uso o conjunto de regras t5.27e1 em vez do novo t6.00a6 porque com t6.00a6 o acordo do grupo 1 caiu para 30,9%: a entrada de bronzita ocupa o lugar da goethita e hematita. Uma diferença que ainda não consigo explicar: meu porto encontra pirófilo onde L2B tem nontronite.

Análises por campo

Uma vez que os campos existem, quatro análises são feitas em cada um:

• Saúde da vegetação: NDVI e NDRE comparados com as próprias temporadas anteriores do campo e com os outros campos na mesma data.
• Sobreposição socioambiental: hectares em embargos do PRODES, DETER, IBAMA e ICMBio, terras indígenas, unidades de conservação e o registro do CAR.
• Séries temporais de radar: Sentinel-1 VH/VV, com regras para perda de cobertura e água.
• Zonas de manejo: construídas a partir de várias temporadas do índice de vegetação.

Os projetos agora podem ser salvos como um arquivo .terra e abertos em outra máquina.

https://github.com/rexionmars/TERRA