Hello, I am pretty new to InSAR techniques and I have run into a problem with my unwrapped imagery. I have no clue why but my output has incorrect borders such as in the attached image. I have no clue what is causing this so any ideas would be greatly appreciated :)

NASA’s feature, Autumn Paints Poland’s Bieszczady Mountains, offers a beautiful example of how Earth observation can capture seasonal changes across a landscape.

Autumn colors are more than a visual spectacle. Satellite imagery can help researchers observe patterns in vegetation and study how landscapes change over time.

Earth observation is often discussed in the context of urban development, infrastructure, agriculture, or disaster response, but monitoring seasonal changes in natural environments is another important application.

For those working with satellite imagery: how do you use Earth-observation data to monitor vegetation or seasonal changes? Do you typically compare imagery captured at different times of the year, or combine satellite data with other sources?

NASA’s article: https://science.nasa.gov/earth/earth-observatory/autumn-paints-polands-bieszczady-mountains/

LandEye app: https://app.landeye.com/dashboard

LandEye: https://landeye.com

Every time I need something useful for analysis, I end up bouncing between 10 different sites: Copernicus, EarthExplorer, a commercial portal or two, plus whatever cloud catalog has the right collection. And each one has its own way of defining your AOI (draw a box, upload a shapefile, paste GeoJSON, pick a tile ID) and its own cloud cover filter if you're after spectral data. Half the time goes into learning each portal's quirks instead of doing the actual analysis. Does anybody else feel like decentralization is one of the biggest bottlenecks?

Upfront disclosure: I work at Picterra, and we built this.

We wanted to show what you can get from stacking public datasets rather than just talk about it, so we put this together and made it free with no sign-up, because gating it would have defeated the point.

Pick anywhere, zoom in, and it returns erosion risk, fire and flood history, land cover, climate projections to the 2040s, land conversion emissions, and forest change since 2020. Sources include Dynamic World, MODIS burned area, Copernicus DEM, JRC Global Surface Water, and the Google forest typology layer. All of them are listed in the app.

One thing worth knowing before you try it: it frames the analysis area by zoom level rather than detecting parcel boundaries, so the numbers describe the frame you are looking at rather than a specific plot. That was a deliberate scope decision, not an oversight, but it changes how you should read the output.

Happy to answer anything on methodology or sources.

lens.picterra.ai

When assessing the impact of a flood, knowing what the area looked like before the event is just as important as seeing the damage afterward.

For this case study, we compared satellite imagery of Prachin Buri, Thailand, from October 7, 2025, with imagery from October 2, 2026, showing flood conditions along the Bang Pakong River.

The comparison highlights an important principle in geospatial analysis: you can't reliably understand change without a reference.

Historical imagery can help analysts:

• Establish baseline land conditions
• Identify changes in flooded areas
• Assess potential impacts on agricultural land and infrastructure
• Support future disaster risk assessments

One important consideration is timing. A historical image provides context, but it doesn't automatically establish the exact conditions immediately before a disaster.

For those working in GIS, remote sensing, or disaster management: how do you typically establish a baseline when assessing flood impacts? Do you rely on historical optical imagery, radar data, or a combination of both?

Hi! I need to do a classification of forest species for an area. Unluckily, I don't have enough polygons to train a model to do it, but I have 2 classifications done in the two neighbouring regions (one very big in the north, one of the same dimension of my target area in the south). I don't have the training polygons of these two neighbouring areas.

Since both existing maps should carry enough information to classify my area (same forest types, same morphological conditions etc), I was considering using them as predictors, together with other bands. I should have then some hundred points in my area to validate the obtained map.

Since my main field is not RS, I want to ask you if it something acceptable from the methodological point of view, and most of all if there is a way to account for the native uncertainties of the maps I use for the training together with new uncertainties derived from my model.

Thank you! If it is something no remotesensing expert would ever do, pls don't insult me

bye!

https://github.com/sekaikx/lifeline

🚨 NEW RESEARCH 🔥
We mapped the 2025 Palisades Fire to explore how wildfire burn severity intersected with the demographic characteristics of the affected communities.
JPG

Spatiotemporal Patterns of Wildfire Severity and Demographic Characteristics in the 2025 Palisades Fire, Los Angeles, California https://www.mdpi.com/4123346 #mdpigeohazards via @GeohazardsM

Hey everyone,

I've been playing around with satellite imagery and ended up building this little project to look at possible changes in forest cover around Koh Phangan, Thailand.

It compares satellite images from 2019 and 2026 to flag areas that might be worth checking on the ground, specially by rangers and authorities. I've added the terrain altitud to get a better idea of where water might flow and how that lines up with the waterways on the map. I'm now wondering if there's something useful I could do with this for flood monitoring or drainage, although I'm still figuring out what might actually be possible.

https://koh-phangan-forest-watch.vercel.app/

I'd love to do some humanitarian work and NGOs, so this has been a way to learn by building something practical I guess.

It's still a work in progress, and the results aren't verified forest loss. I'd be curious to hear what people think, could something like this be useful in conservation or environmental work? Any feedback is welcome.

Hi everyone,

I’m an undergraduate civil engineering student working on a research project involving multi-temporal landslide mapping along Nepal’s Mid-Hill Highway.

My goal is to develop a reliable landslide inventory covering approximately 2010–2023, identifying landslide locations, mapping their boundaries, and determining when they first appeared or reactivated based on historical satellite imagery.

I’ve been exploring Google Earth Engine (GEE), QGIS, Sentinel-2, and Landsat imagery, but I’m struggling to determine the most efficient and reliable workflow for mapping landslides across such a large area and multiple years.

I would appreciate advice on the following:

1. Mapping methodology: What is the most practical approach for creating a multi-temporal landslide inventory over a long highway corridor? Is manual digitization, semi-automated detection, or a combination of both preferable?
2. Historical imagery: What datasets or platforms would you recommend for identifying landslides from 2010 onward? In particular, are there suitable alternatives to Sentinel-2 for the pre-2015 period?
3. Change detection: Are there reliable methods for identifying newly formed landslide scars by comparing satellite images from different dates?
4. Existing inventories: Does anyone know of publicly available landslide inventories covering Nepal’s Mid-Hill Highway or nearby regions, particularly those containing temporal information?
5. Practical workflow: If you have worked on a similar project, what workflow would you recommend for mapping landslide polygons and estimating their occurrence periods without manually inspecting every image?

I’m particularly interested in approaches that are feasible for a student research project with limited computational resources.

I’m not expecting a fully automated solution. My aim is to develop a scientifically defensible inventory using satellite-based candidate detection and manual verification wherever necessary.

Any advice, relevant publications, open datasets, scripts, or examples of similar projects would be extremely helpful. I’d also appreciate hearing from researchers who have worked on landslide inventories in Nepal or the Himalayas.

Thank you!

Hey everyone,

I've been playing around with satellite imagery and ended up building this little project to look at possible changes in forest cover around Koh Phangan, Thailand.

It compares satellite images from 2019 and 2026 to flag areas that might be worth checking on the ground, specially by rangers and authorities. I've added the terrain altitud to get a better idea of where water might flow and how that lines up with the waterways on the map. I'm now wondering if there's something useful I could do with this for flood monitoring or drainage, although I'm still figuring out what might actually be possible.

https://koh-phangan-forest-watch.vercel.app/

I'd love to do some humanitarian work and NGOs, so this has been a way to learn by building something practical I guess.

It's still a work in progress, and the results aren't verified forest loss. I'd be curious to hear what people think, could something like this be useful in conservation or environmental work? Any feedback is welcome.

Hey everyone,

For those who process Orthophotos from aerial imageries at work, what tools/softwares do you use? Proprietary or free software? Is there any tool(s) in the Python ecosystem?

I have got hands-on with some aerial imagery and am looking for software options for deriving orthophotos from it.

Thanks!

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?