RL-Driven Sustainable Land-Use Allocation for the Lake Malawi Basin
arXiv:2604.03768v1 Announce Type: new Abstract: Unsustainable land-use practices in ecologically sensitive regions threaten biodiversity, water resources, and the livelihoods of millions. This paper presents a deep reinforcement learning (RL) framework for optimizing land-use allocation in the Lake Malawi Basin to maximize total ecosystem service value (ESV). Drawing on the benefit transfer methodology of Costanza et al., we assign biome-specific ESV coefficients -- locally anchored to a Malawi wetland valuation -- to nine land-cover classes derived from Sentinel-2 imagery. The RL environment models a 50x50 cell grid at 500m resolution, where a Proximal Policy Optimization (PPO) agent with action masking iteratively transfers land-use pixels between modifiable classes. The reward function
View PDF HTML (experimental)
Abstract:Unsustainable land-use practices in ecologically sensitive regions threaten biodiversity, water resources, and the livelihoods of millions. This paper presents a deep reinforcement learning (RL) framework for optimizing land-use allocation in the Lake Malawi Basin to maximize total ecosystem service value (ESV). Drawing on the benefit transfer methodology of Costanza et al., we assign biome-specific ESV coefficients -- locally anchored to a Malawi wetland valuation -- to nine land-cover classes derived from Sentinel-2 imagery. The RL environment models a 50x50 cell grid at 500m resolution, where a Proximal Policy Optimization (PPO) agent with action masking iteratively transfers land-use pixels between modifiable classes. The reward function combines per-cell ecological value with spatial coherence objectives: contiguity bonuses for ecologically connected land-use patches (forest, cropland, built area etc.) and buffer zone penalties for high-impact development adjacent to water bodies. We evaluate the framework across three scenarios: (i) pure ESV maximization, (ii) ESV with spatial reward shaping, and (iii) a regenerative agriculture policy scenario. Results demonstrate that the agent effectively learns to increase total ESV; that spatial reward shaping successfully steers allocations toward ecologically sound patterns, including homogeneous land-use clustering and slight forest consolidation near water bodies; and that the framework responds meaningfully to policy parameter changes, establishing its utility as a scenario-analysis tool for environmental planning.
Comments: 7 pages, 5 figures
Subjects:
Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2604.03768 [cs.AI]
(or arXiv:2604.03768v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2604.03768
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Ying Yao [view email] [v1] Sat, 4 Apr 2026 15:39:33 UTC (5,367 KB)
Sign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
modelannounceservice
Adobe CEO Shantanu Narayen Steps Down: The Subscription Empire He Built
After 18 years at the helm, Shantanu Narayen is stepping down as Adobe's CEO. The news dropped this week, and the creative industry collectively shrugged. That reaction — or lack thereof — tells you everything about what happened to Adobe under his watch. The Subscription King Let's give credit where it's due: Narayen turned Adobe into a money machine. When he pushed the company from boxed software to Creative Cloud subscriptions in 2013, Wall Street loved it. Recurring revenue! Predictable growth! The stock went from around $35 to over $500. Designers, photographers, and video editors? They were less thrilled. Overnight, owning your tools became renting them. Miss a payment, lose access to your own work. The internet raged. Adobe didn't flinch. Here's the thing — the subscription model di
250 Clones in 4 Days: A Student's Journey Building an AI Security Tool
🚀 250 Clones in 4 Days: A Student's Journey Building an AI Security Tool By Nasarah Peter Dashe Cybersecurity Student @ UNIJOS | Founder of Permi The Numbers That Surprised Me On April 2nd, 2026, I did something terrifying. I typed pip install permi into my terminal, ran a few final tests, and hit publish on PyPI. A vulnerability scanner built by a student with no funding, no team, and no prior accomplishments was now available for anyone in the world to download. Four days later, GitHub told me something I didn't expect: 250 clones. 62 developers per day, on average, downloading Permi. Testing it. Breaking it. Some even giving feedback. This isn't a Silicon Valley startup with millions in backing. This is a cybersecurity student at the University of Jos, building in public, one commit at
React Server Components: What Actually Changes in Your Architecture
Server Components Aren't Just an Optimization Most articles treat React Server Components (RSC) as a performance feature. They're not—they're an architectural shift in where your application logic lives. Understanding this changes how you design everything. The Core Distinction // Server Component — runs on server, never shipped to browser // app/users/page.tsx async function UsersPage () { // Direct DB access — no API needed const users = await db . users . findMany ({ where : { active : true }, orderBy : { createdAt : ' desc ' }, }); return ( div > { users . map ( user => ( UserCard key = { user . id } user = { user } / > ))} /div > ); } // Client Component — runs in browser, can use hooks and events ' use client ' ; function UserCard ({ user }: { user : User }) { const [ expanded , setE
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Releases
Google Open Sources Experimental Multi-Agent Orchestration Testbed Scion
Designed to manage concurrent agents running in containers across local and remote compute, Scion is an experimental orchestration testbed that enables developers to run groups of specialized agents with isolated identities, credentials, and shared workspaces. By Sergio De Simone
Adobe CEO Shantanu Narayen Steps Down: The Subscription Empire He Built
After 18 years at the helm, Shantanu Narayen is stepping down as Adobe's CEO. The news dropped this week, and the creative industry collectively shrugged. That reaction — or lack thereof — tells you everything about what happened to Adobe under his watch. The Subscription King Let's give credit where it's due: Narayen turned Adobe into a money machine. When he pushed the company from boxed software to Creative Cloud subscriptions in 2013, Wall Street loved it. Recurring revenue! Predictable growth! The stock went from around $35 to over $500. Designers, photographers, and video editors? They were less thrilled. Overnight, owning your tools became renting them. Miss a payment, lose access to your own work. The internet raged. Adobe didn't flinch. Here's the thing — the subscription model di
On the Rate Region of I.I.D. Discrete Signaling and Treating Interference as Noise for the Gaussian Broadcast Channel
arXiv:2604.04092v1 Announce Type: cross Abstract: We revisit the Gaussian broadcast channel (GBC) and explore the rate region achieved by purely discrete inputs with treating interference as noise (TIN) decoding. Specifically, we introduce a simple scheme based on superposition coding with identically and independently distributed (i.i.d.) inputs drawn from discrete constellations, e.g., pulse amplitude modulations (PAM). Most importantly, we prove that the resulting achievable rate region under TIN decoding is within a constant gap to the capacity region of the GBC, where the gap is independent of all channel parameters. In addition, we show via simulation that the weak user can achieve a higher rate with PAM than with Gaussian signaling in some cases.
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!