An evaluation of void‐filling interpolation methods for SRTM data

By Hannes Isaak Reuter, Andy Nelson, Andrew Jarvis. International Journal of Geographical Information Science, 2007

880 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Environmental Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/b43636a459095ab30606d19f8d86340eb26be176

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Achim Zeileis
2. Jakob W. Messner
3. Georg J. Mayr
4. Susan Athey
5. Francis L. Huang
6. Wei-Yin Loh
7. Francisco Cribari‐Neto
8. Max Kuhn

In Environmental Engineering

1. Achim Zeileis
2. Jakob W. Messner
3. Georg J. Mayr

Citations in the corpus

• The environmental-data automated track annotation (Env-DATA) system: linking animal tracks with environmental data (2013, neutral): “SRTM [43] High resolution topography NASA http://www.”
• Spatial ensemble post‐processing with standardized anomalies (2016, methodology): “To calculate spatial climatologies, a digital elevation model with a spatial resolution of 90 m from the Shuttle Radar Topography Mission (SRTM: Reuter et al., 2007) is used.”
• Production of leaf wax n-alkanes across a tropical forest elevation transect (2016, neutral): “Elevation profile: gray line (elevation acquired 840 from the Shuttle Radar Topographic Mission (SRTM) 90m Digital Elevation Database 4.1 841 (Reuter et al., 2007), black line (smoothed elevation), gray envelope (topography; ± 2σ elevation 842 from 1 km wide swath perpendicular to transect,…”