Spring 2027

Environmental Sensing Technologies

Survey of diverse environmental monitoring technologies, including remote sensing, UAVs, and social media, with hands-on experience in geocomputing, coding, and cloud-based data analytics.

offering recorded3 credits
Recorded instructors · Spring 2027 Annemarie Schneider3.7/5

Summary

1 / 3

Recent recorded grades — Spring 2024: 3.88 GPA, 100.0% A/AB (n=8 letter grades); Spring 2025: 4.00 GPA, 100.0% A/AB (n=6 letter grades); Spring 2026: 3.88 GPA, 100.0% A/AB (n=13 letter grades).

What you’ll be able to do

  • Demonstrate their understanding of major theories, approaches, concepts, and methods in remote sensing science.

  • Apply knowledge of the nature and properties of electromagnetic radiation and how it is affected by interactions with the atmosphere and the Earth’s surface.

  • Identify the techniques, skills, and modern tools necessary for monitoring environmental phenomena with earth observation data.

  • Identify and appropriately utilize data types from the optical, thermal, and microwave portions of the electromagnetic spectrum, and from a wide range of airborne, satellite and unmanned aerial platforms, including high (greater than 5 m), medium (10-30 m), and coarse (250-1000 m) spatial resolution imagery.

  • Illustrate the utility of machine learning for big data applications in remote sensing, and apply machine learning theories and tools in practice.

  • Apply traditional and new forms of data fusion using earth observation imagery and a wide range of geophysical, social, and economic sources.

  • Identify real-world issues that could benefit from the application of hyper-spectral and hyper-temporal data, and illustrate effective ways to apply these data for each.

  • Analyze and interpret LiDAR point data clouds and LiDAR data derivatives for applications ranging from forestry to urban sustainability.

  • Integrate new sources of data from unmanned aerial vehicles (UAVs) for monitoring biodiversity and conservation, and illustrate ways to influence policy with these findings.

  • Apply a wide range of image processing tools using cloud computing platforms, with particular focus on big data applications.

  • Communicate effectively through discussion, oral presentations, and written reports.

Source: UW Guide

Grade history

↗
average GPA
letter grades
A
AB
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BC
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All recorded terms · compare terms & instructors

Prerequisites

Course map

LAND ARC/​ENVIR ST/​G L E/​GEOG/​GEOSCI 371 and graduate/professional standing (or FW ECOL 371 prior to Summer 2026)

ENVIRST 971

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Professors

Spring 2027
Adjusted rating
RMP difficulty
captured reviews
About this rating

Raw average: 4.00/5 from 7 quality ratings. The adjusted rating blends this with the UW review average (3.66/5), weighted as 20 additional ratings. Smaller samples stay closer to that average. Each captured review is counted once in the prior; this does not correct who chooses to leave a review.

RMP profile ↗ · All captured review dates; profile matched by name.

Recent recorded grades — Spring 2024: 3.88 GPA, 100.0% A/AB (n=8 letter grades); Spring 2025: 4.00 GPA, 100.0% A/AB (n=6 letter grades); Spring 2026: 3.88 GPA, 100.0% A/AB (n=13 letter grades).

Historical instructors & teaching patterns

ANNEMARIE SCHNEIDER is recorded teaching in Spring 2020, Spring 2022, Spring 2023, Spring 2024, Spring 2025, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction0 / 150

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records
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Student experience

the class

Recent recorded grades — Spring 2024: 3.88 GPA, 100.0% A/AB (n=8 letter grades); Spring 2025: 4.00 GPA, 100.0% A/AB (n=6 letter grades); Spring 2026: 3.88 GPA, 100.0% A/AB (n=13 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Traditional remote sensing data sources
  • Unconventional data sources like UAVs and social media
  • Spatial and temporal data management and analytics
  • Geocomputing, coding, and cloud-based platforms
  • UAV data for biodiversity, conservation, and policy

Skills

  • Monitoring environmental phenomena with earth observation data
  • Utilizing multi-spectral and multi-platform remote sensing imagery
  • Applying machine learning to remote sensing big data
  • Analyzing LiDAR point clouds and derivatives
  • Cloud-based image processing for big data
  • Scientific communication via presentations and reports

Grades

Latest available · Spring 2026— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Spring 2026

More grade details Grade mix, volume & source data

Not enough comparable courses for Spring 2027 in UW–Madison.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

Observations at scan time; dates do not imply when a catalog change took effect.

Download part 1
Selected offering source records
ENVIRST 971 · Spring 2027

Environmental Sensing Technologies

Recorded 2026-10-14
Raw records 
[
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:360:025418",
    "course_id": "ENVIRST 971",
    "course_uid": "course_2ca42a08f05366f351c31860",
    "term_id": "1274",
    "source_course_id": "025418",
    "source_subject_id": "360",
    "title": "Environmental Sensing Technologies",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Not Applicable"
  }
]
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Student reviews

Original comments behind the course and instructor summaries.

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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

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Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

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Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "c4f98f941124d1731e55c087912daa5200ab3d7e4d6dc1201b7044b1c6508628",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}