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Methods of Obtaining Topography

Methods of Obtaining Topography

Topography involves measuring and mapping land elevations, natural features, and artificial structures to create accurate representations of the terrain. Topographic surveying relies on traditional and modern methods, each with distinct advantages and limitations.Traditional Surveying Methods:Transit stadia surveys and plane table surveys were widely used traditional surveying methods. These techniques relied on instruments like theodolites and stadia rods for measuring distances and angles,...

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Topographic surveying is critical for documenting the Earth's surface, focusing on capturing elevations, slopes, and natural and man-made features. It is essential in construction planning, water resource management, and land-use analysis. The primary outcome of such surveys is a topographic map, which uses contour lines to visually represent the shape and slope of the terrain, providing valuable insights into the landscape's characteristics.Contour lines are fundamental to understanding the...

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Plotting of Topographic Maps

Topographic maps represent the Earth's surface features using contour lines, which connect points of equal elevation to create a two-dimensional representation of three-dimensional terrain. Creating a topographic map requires a systematic approach.Begin by plotting a scaled grid and marking intersections corresponding to the survey's elevation data points. Assign elevation values at these intersections to build the base map. Next, determine contour levels using a consistent contour interval,...

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Related Experiment Video

Updated: Jun 27, 2026

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

Automated Mapping of Periglacial Landforms on Mars' Utopia Planitia Using a Multi-Scale Texture-Enhanced U-Net.

Xiaoyi Chang¹, Shuanggen Jin^1,2, Yanchao Zheng¹

¹School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China.

Sensors (Basel, Switzerland)

|June 26, 2026

Summary

Automated mapping of Martian periglacial landforms in Utopia Planitia was achieved using a novel MTU-Net model. This approach enhances the investigation of ground-ice, climate history, and potential habitability on Mars.

Keywords:

CTX MTU-Net Utopia Planitia periglacial landform

Related Experiment Videos

Last Updated: Jun 27, 2026

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

Area of Science:

Planetary Science
Geomorphology
Artificial Intelligence

Background:

Martian periglacial landforms offer crucial insights into ground-ice, climate evolution, and habitability.
Utopia Planitia is rich in ice-related features, making it ideal for periglacial mapping.
Manual interpretation of these features is labor-intensive and inconsistent across large areas.

Purpose of the Study:

To develop an automated and standardized method for mapping periglacial landforms in Utopia Planitia.
To create a dataset of four key landform types (flat-floored depressions, thermal contraction cracks, scalloped depressions, brain terrain) from Context Camera (CTX) imagery.
To improve the efficiency and consistency of periglacial feature identification on Mars.

Main Methods:

A Multi-scale Texture-enhanced U-Net (MTU-Net) deep learning model was developed for semantic segmentation.
The MTU-Net incorporates hierarchical attention and multi-scale texture enhancement for improved feature recognition.
The model was trained and evaluated on a dataset of CTX imagery from Utopia Planitia.

Main Results:

MTU-Net achieved high performance metrics: 89.55% mean intersection over union (mIoU), 94.71% mean F1-score, and 91.21% Kappa coefficient.
The model outperformed a baseline U-Net, demonstrating its effectiveness in complex Martian terrain.
Generated regional maps revealed significant spatial variations in landform distribution across Utopia Planitia.

Conclusions:

The developed MTU-Net provides an effective automated solution for mapping Martian periglacial landforms.
This methodology facilitates large-scale, consistent analysis of geomorphological features related to ground ice.
The study lays the groundwork for advanced automated mapping of periglacial environments on Mars, aiding in the search for past or present life.