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

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!

I’m working with Galileo NIMS data from Europa, specifically the GO_1116 and GO_1117 observations, and I’m trying to understand how reliable these datasets are for hyperspectral anomaly detection.

I’d like to know:

1. How well calibrated are the GO_1116 and GO_1117 NIMS cubes for spectral analysis?

2. How much instrumental noise or calibration-related artifacts should I expect?

3. Are there known problematic wavelengths/bands or bad pixels that should be removed or masked?

4. Could apparent spectral anomalies be caused by the instrument or data processing rather than actual surface variations?

5. Are the `*_core.cub` products appropriate for this type of analysis?

6. Is there a recommended preprocessing or calibration workflow before performing spectral analysis?

7. How confidently can unusual spectral signatures in these datasets be interpreted as real surface/compositional differences?

I’d especially appreciate input from anyone who has worked with Galileo NIMS data, GO_1116/GO_1117, or Europa spectroscopy.

Any recommendations for relevant calibration documentation or papers would also be really helpful. Thanks!

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.